A Tunable Ferrofluid-based Polydimethylsiloxane (PDMS) Microchannel Inductor for Ultra High Frequency Applications

In this work, a tunable ferrofluid-based polydimethylsiloxane (PDMS) microchannel inductor with high quality factor and high tuning range is proposed. For this project, PDMS is used to create a microchannel with a width and height of 0.53 mm and 0.2 mm respectively. The microchannel is then used to cover the whole design of a solenoid inductor. A solenoid inductor is designed using wire bonding technique where lines of copper and bond wires are used to form a solenoid winding on top of silicon substrate. A light hydrocarbon based ferrofluid EMG 901 660 mT with high permeability of 5.4 is used. The ferrofluid-based liquid is injected into the channel to enhance the performance of a quality factor. A 3D full-wave electromagnetic fields tool, ANSYS HFSS is used in this work to simulate the solenoid inductor. The results obtained in this work gives a quality factor of more than 10 at a frequency range of 300 MHz to 3.3 GHz (Ultra High Frequency range). The highest quality factor is 37 which occurs at a frequency of 1.5 GHz, provides a high tuning range of 112%

effect of basalt fibres reinforcement and aluminum trihydrate on the thermal properties of intumescent fire retardant coatings

This research is carried out in order to study the synergistic effect of aluminium trihydrate and basalt fibres on the properties of fire resistant intumescent coatings. Intumescent fire retardant coatings were developed using different flame retardants such as ammonium polyphosphate, expandable graphite, melamine and boric acid. These flame retardants were bound together with the help of epoxy binder along with curing agent. Furthermore, individual and combinations of aluminium trihydrate and basalt fibres was incorporated in the formulations to analyse mechanical and chemical properties of the coatings. Char expansion was observed using furnace test, thermogravimetric analysis was used to determine residual weight, X-Ray Diffraction was performed to investigate compounds present in the char, shear test was conducted to determine char strength and scanning electron microscopy analysis was performed to observe morphology of the burnt char. From the microscopic investigation it was concluded that the dense structure of the char increased the char integrity by adding basalt and aluminium trihydrate as fillers. X-Ray Diffraction results shows the presence boron phosphate, and boric acid which enhanced the thermal performance of the coating up to 800°C. From the Thermogravimetric analysis it was concluded that the residual weight of the char was increased up to 34.9 % for IC-B2A4 which enhanced thermal performance of intumescent coating

Enhancing PDC Functional Connectivity Analysis for Subjects with Dyslexia Using Artifact Cancellation Techniques

The neurobiological origin of dyslexia allows the study of this disorder by examining functional con- nectivity between regions of the brain. During rest-state or at task completion, Electroencephalograms (EEG) are used to observe brain signals. By using Partial Directed Coherence (PDC) analysis, the correct anal- ysis of functional connectivity was assessed. In spite of that, the estimation of functional connectivity can be inaccurate due to the presence of artifacts. Several methods have been employed by researchers to remove artifacts, including Moving Average Filters (MAF), Wiener Filters (WF), Wavelet Transforms (WT), and hybrid filters. Despite this, no research has been con- ducted on the effects of artifact removal methods on functional connectivity. Consequently, Artifact Can- cellation (AC) algorithms are developed to reduce the effects of eye blinks, eye movements, and muscle move- ments on functional connectivity estimation. In this work, the denoising filters discussed earlier are utilized as part of the AC algorithm. Additionally, a compar- ison was conducted to determine the effectiveness of the filters. According to the results, AC-MAF removed all artifacts with the least computational complexity after improving the MAF. In order to test its efficacy in real-world conditions, it was applied to the real signals recorded while children with dyslexia were participat- ing in rapid automatized naming activities. Utilizing the PDC approach, the developed algorithm accurately assessed functional connectivity

A novel anatomical lung phantom and its applications in lung perfusion scans for pulmonary embolism diagnosis

Numerous commercial and non-commercial nuclear medicine imaging phantoms are used for quality assurance studies, teaching, training, and research. Commercially available phantoms are sometimes not applicable in specific medical studies. As non-commercial phantoms are not easily accessible, many researchers have chosen to construct their own distinct phantom. This thesis describes the design, development and characteristics of a novel anatomical lung phantom. The phantom was uniquely developed to model perfusion conditions of patients with suspected pulmonary emboli. Two imaging modalities were chosen in this study: planar imaging and Single Photon Emission Computed Tomography (SPECT) imaging. Applications of the phantom as a quality assurance (QA) device and as a training and teaching tool were studied. The phantom was used as a QA tool to compare the interpretations of Nuclear Medicine physicians on lung perfusion and ventilations scans using the revised Prospective Investigation of Pulmonary Embolism Diagnosis (PIOPED) criteria. The studies showed both the correct and incorrect diagnosis of pulmonary embolism by the physicians. A comprehensive lung perfusion imaging atlas is also included in the thesis. A close ended Likert-scale survey was carried out to study the significance of the imaging atlas to Nuclear Medicine physicians. Nuclear Medicine physicians acknowledged the ability of the atlas to help in training new Nuclear Medicine physicians. The atlas was classified as an adequate teaching material for the physicians who are developing experience to diagnose lung perfusion and ventilation scans. In summary, the thesis demonstrates an original contribution to the nuclear medicine imaging field through the development of the novel anatomical lung phantom

Resolving Gender Difference in Problem Solving Based On the Analysis of Electroencephalogram (EEG) Signals

Problem solving is regarded as one of the core work-related abilities and skills, which are highly demanded by the workplace and industry. Current literature suggests that problem solving abilities might differ from one individual to another due to biological factors such as brain activationa, cognitive functions and hormones, as well as due to socio-cultural and socio-economic factors like gender roles, self-perceptions and stereotyping. Hence, this study used electroencephalogram (EEG) signals to investigate the differences in problem solving skills among the Malaysian undergraduates based on their gender differences. 29 undergraduate students from the Faculty of Electrical Engineering, Universiti Teknologi Malaysia (UTM) served as the subjects of the experiments in this research. Specifically, 16 female and 13 male subjects engaged in two main problem-solving tasks: mental arithmetic task and Tower of Hanoi (TOH) task. The EEG data were analysed using partial directed coherence (PDC) and power spectrum estimation (PSE). Based on the results, female subjects achieved only 1% higher performance in mental arithmetic task, while male subjects achieved about 13% higher performance in TOH task. The differences in terms of the functional connectivity between brain regions, i.e. in PDC, as well as the power distribution of 6 EEG waveforms, i.e. delta, theta, alpha, beta, gamma and high gamma bands are also highlighted and represented graphically in this paper

Design and fabrication of pulmonary embolism phantom for planar and SPECT V/Q imaging quality assurance

Planar and Single Photon Emission Computed Tomography (SPECT) scintigraphy are the two main modalities for pulmonary embolism (PE) diagnosis via lung ventilation/perfusion (V/Q) scans. This study aims to develop an anatomical lung phantom for the quality assurance (QA) of V/Q scans using planar and SPECT imaging. The phantom consists of two hollow anatomical lung cavities and 20 solid anatomical bronchopulmonary segments. The phantom functions as a PE simulator by enabling an interchangeable perfusion defect, represented by a solid anatomical bronchopulmonary segment, to be introduced into each of the lung cavities. These cavities are filled with expanded polystyrene (EPS) beads immersed in a 99mTc solution, which simulates the alveoli. The anatomical ‘dead space’ due to the solid introduced segment represents a perfusion defect in lung V/Q scans. In this study, a sample anatomical PE event was simulated. The phantom was prepared with a perfusion defect within the posterior basal segment in the left lung. Images were acquired for subsequent qualitative analysis. This study has demonstrated promising results in the simulation of PE events in lungs. Further development is warranted for the phantom to be used as a viable QA tool in V/Q lung scanning using planar or SPECT imaging

Compact Convolutional neural network (CNN) based on SincNet for end-to-end motor imagery decoding and analysis

In the field of human-computer interaction, the detection, extraction and classification of the electroencephalogram (EEG) spectral and spatial features are crucial towards developing a practical and robust non-invasive EEG-based brain-computer interface. Recently, due to the popularity of end-to-end deep learning, the applicability of algorithms such as convolutional neural networks (CNN) has been explored to achieve the mentioned tasks. This paper presents an improved and compact CNN algorithm for motor imagery decoding based on the adaptation of SincNet, which was initially developed for speaker recognition task from the raw audio input. Such adaptation allows for a compact end-to-end neural network with state-of-the-art (SOTA) performances and enables network interpretability for neurophysiological validation in cortical rhythms and spatial analysis. In order to validate the performance of proposed algorithms, two datasets were used; the first is the publicly available BCI Competition IV dataset 2a, which was often used as a benchmark in validating motor imagery classification algorithms, and the second is a dataset consists of primary data initially collected to study the difference between motor imagery and mental-task associated motor imagery BCI and was used to test the plausibility of the proposed algorithm in highlighting the differences in terms of cortical rhythms. Competitive decoding performance was achieved in both datasets in comparisons with SOTA CNN models, albeit with the lowest number of trainable parameters. In addition, it was shown that the proposed architecture performs a cleaner band-pass, highlighting the necessary frequency bands that were crucial and neurophysiologically plausible in solving the classification tasks

Mental imagery classification using one-dimensional convolutional neural network for target selection in single-channel BCI-controlled mobile robot

This paper introduces the use of the one-dimensional convolutional neural network (1D-CNN) for end-to-end EEG decoding with application towards a BCI system with a shared control scheme. In general, subjects wearing a single-channel EEG electrode located at F8 (10–20 international standards) were required to perform mental tasks by mentally visualising the rotation of a star and mind relaxation at a specific time and by robot orientation. The visualisation of a rotating star suggests that the mobile robot is currently oriented towards a target, thus enabling target selection. We showed that proposed classifier obtained the best accuracy of 92.09% in classifying the subject’s performing mental rotation task or mental relaxation when compared with conventional classification methods such as support vector machine—75.69%, Kth-nearest neighbour—65.50% and linear discriminant analysis—65.20%. Furthermore, different from conventional methods, the use of 1D-CNN enables end-to-end learning, that is the automatic decoding of EEG signals without requiring feature selection or extraction. To validate that the proposed classifier performs better than conventional methods, the extracted kernel weights of proposed 1D-CNN filters were visualised as a temporal plot, and spectral analysis was performed on the extracted weights. The obtained results confirmed that the proposed 1D-CNN was able to generate filters that resemble the EEG wave patterns of different frequencies and spectral analysis confirmed that the filters exploited information from multiple frequency bands (such as alpha band and beta band) that are often associated with a heightened mental state when performing mental tasks