Dynamical control of one- and two-dimensional optical fibre scanning

This thesis investigates the dynamical control of one- and two-dimensional optical fibre scanning. One dimensional scanning is performed with a mechanically biaxial polarisation-preserving fibre mounted on a piezoelectric transducer with one of its principal mechanical axes aligned parallel to the excitation direction. The addition of an apertured reflector in front of the imaging lens allows a position sensing mechanism based on intermittent optical feedback to be integrated into the scanner. Over-scanning the lens generates timing pulses interlaced with back-scattered signals from the target. The timing information can be used for closed loop control of the phase and amplitude of vibration. Suitable control algorithms are developed and their convergence and stability is studied. This thesis also investigates the construction of fibres with enhanced mechanically asymmetry and their dynamical properties during two-dimensional imaging based on Lissajous scan patterns. Dip-coating is proposed as a method of forming two-cored waveguide cantilevers from two separate, parallel fibres that are encapsulated in a plastic coating. The frequency ratio between the two orthogonal bending mode resonances can be controlled with number of coatings. An exact image reconstruction algorithm based on Lissajous scanning is proposed. Latency, transient response and steady-state phase errors are all shown to cause dramatic deterioration of the reconstructed image. Solutions are provided by ensuring the correct starting time for data acquisition and introducing a drive phase correction to one of the axes. Two methods of resolution enhancement are demonstrated. The first is based on combining data sets obtained during separate scans carried out with deliberately applied phase offsets. The second operates by combining data sets from separate imaging operations carried out using the two different fibre cores. Finally, this thesis demonstrates potential applications in optogenetics by combining the two operations of imaging and writing, using different light sources that may also have different wavelengths.Open Acces

Integration of 3D printing in computer-aided design and engineering course

Engineering students at an undergraduate level typically learn the design aspect and concept through lectures and practical sessions using computeraided software. However, the current computer-aided design and engineering (CAD/CAE) course did not expose the students to apply and relate the latest advanced technologies to solve global issues, for instance as listed in the United Nations Sustainable Development Goals (UN SDG). Therefore, an improved CAD/CAE course taken by the students of the Electrical and Electronic Engineering Programme in Universiti Kebangsaan Malaysia integrates 3D printing and conduct their project based on UN SDG themes. A total of 22 projects was produced, which involves both mechanical and electrical design with some of the physical models were 3D printed. Thus, students able to strengthen their understanding of the design concept through the integration of 3D printing and simultaneously aware of the current global issues

Label-free detection of dissolved carbon dioxide utilizing multimode tapered optical fiber coated zinc oxide nanorice

A label-free detection for dissolved carbon dioxide (dCO 2 ) is developed using a tapered optical fiber sensor. The tapered region of the optical fiber is coated with the zinc oxide (ZnO) nanorice and used as a probe for dCO 2 sensing. The sensor probe was exposed to different concentrations of dCO 2 solution ranging from 10 to 100 ppm. ZnO nanorice can adsorb dCO 2 via strong hydrogen bonding due to the presence of plenty of oxygen atoms on its surface layer. The interaction between ZnO nanorice and dCO 2 changes the optical properties of the ZnO nanorice layer, resulting in the change in reflectance. From the experiment, the result shows that there is an improvement in the sensitivity of the sensor when higher concentration was used. A broad linear trend ranging from 0 to 60 ppm ( R2=0.972 ) is observed for the sensor probe that is coated with 1.0 M of ZnO nanorice compared with the 0.1 M and 0.5 M ZnO nanorice concentrations. The sensor sensitivity obtained is 0.008 mW/ppm. The sensor demonstrates a response and recovery time of 0.47 and 1.70 min, respectively. Good repeatability is obtained with the standard deviation in the range of 0.008–0.027. The average resolution calculated for this sensor is 4.595 ppm

Effect of Active Site Modification towards Performance Enhancement in Biopolymer κ-Carrageenan Derivatives

This research demonstrates a one-step modification process of biopolymer carrageenan active sites through functional group substitution in κ-carrageenan structures. The modification process improves the electronegative properties of κ-carrageenan derivatives, leading to enhancement of the material’s performance. Synthesized succinyl κ-carrageenan with a high degree of substitution provides more active sites for interaction with analytes. The FTIR analysis of succinyl κ-carrageenan showed the presence of new peaks at 1068 cm−1, 1218 cm−1, and 1626 cm−1 that corresponded to the vibrations of C–O and C=O from the carbonyl group. A new peak at 2.86 ppm in 1H NMR represented the methyl proton neighboring with C=O. The appearance of new peaks at 177.05 and 177.15 ppm in 13C NMR proves the substitution of the succinyl group in the κ-carrageenan structure. The elemental analysis was carried out to calculate the degree of substitution with the highest value of 1.78 at 24 h of reaction. The XRD diffractogram of derivatives exhibited a higher degree of crystallinity compared to pristine κ-carrageenan at 23.8% and 9.2%, respectively. Modification of κ-carrageenan with a succinyl group improved its interaction with ions and the conductivity of the salt solution compared to its pristine form. This work has a high potential to be applied in various applications such as sensors, drug delivery, and polymer electrolytes

Program pementoran STEM di kalangan pelajar prasiswazah bagi pertandingan robotik MakeX

Pertandingan robotik MakeX adalah salah satu pertandingan robotik terbesar di Malaysia, yang melibatkan peserta dari kalangan pelajar sekolah rendah dan menengah. Penganjuran pertandingan ini adalah unik berbanding dengan pengajuran pertandingan robotik lain, di mana, peserta yang bakal bertanding diberi latihan dan bengkel mengenai asas pemasangan dan pengatucaraan robot serta strategi dalam menyelesaikan tugasan pertandingan. Crystal UKM, selaku pengajur bersama pertandingan ini diberi tanggungjawap untuk menyediakan latihan kepada peserta-peserta pertandingan yang terdiri daripada pelajar sekolah berusia seawal 7 tahun hingga 17 tahun. Dalam menyediakan modul berkesan kepada peserta yang bertanding pada tahun ini, pihak Crystal UKM telah mengolah satu program pementoran yang unik melibatkan pelajar-pelajar prasiswazah tahun dua dan tiga, dari program Kejuruteraan Elektrik dan Elektronik. Pelajar-pelajar prasiswazah ini disamping bertanggungjawap memberi latihan teknikal, mereka juga berperanan untuk memaksimumkan potensi dan mengembangkan kemahiran peserta, bagi meningkatkan prestasi peserta disamping meningkatkan daya saing dikalangan peserta. Kertas kerja ini memberi tumpuan kepadaperanan dan perspektif pelajar-pelajar sebagai mentor kepada peserta pertandingan, serta mengkaji keberkesanan program pementoran ini dalam meningkatkan kemahiran insaniah pelajar yang terlibat sebagai mentor. Kejayaan pasukan Malaysia merangkul pingat emas dalam pertandingan robotic MakeX peringkat antarabangsa, di Guangzhou, China baru-baru ini, dan pencapaian membanggakan 2 lagi pasukan yang tergolong dalam 20 pasukan teratas, membuktikan keberkesanan program pementoran yang dijalankan

Human Body Performance with COVID-19 Affectation According to Virus Specification Based on Biosensor Techniques

Life was once normal before the first announcement of COVID-19’s first case in Wuhan, China, and what was slowly spreading became an overnight worldwide pandemic. Ever since the virus spread at the end of 2019, it has been morphing and rapidly adapting to human nature changes which cause difficult conundrums in the efforts of fighting it. Thus, researchers were steered to investigate the virus in order to contain the outbreak considering its novelty and there being no known cure. In contribution to that, this paper extensively reviewed, compared, and analyzed two main points; SARS-CoV-2 virus transmission in humans and detection methods of COVID-19 in the human body. SARS-CoV-2 human exchange transmission methods reviewed four modes of transmission which are Respiratory Transmission, Fecal–Oral Transmission, Ocular transmission, and Vertical Transmission. The latter point particularly sheds light on the latest discoveries and advancements in the aim of COVID-19 diagnosis and detection of SARS-CoV-2 virus associated with this disease in the human body. The methods in this review paper were classified into two categories which are RNA-based detection including RT-PCR, LAMP, CRISPR, and NGS and secondly, biosensors detection including, electrochemical biosensors, electronic biosensors, piezoelectric biosensors, and optical biosensors

Evaluating Performance of Machine Learning Models for Diabetic Sensorimotor Polyneuropathy Severity Classification using Biomechanical Signals during Gait

Diabetic sensorimotor polyneuropathy (DSPN) is one of the prevalent forms of neuropathy affected by diabetic patients that involves alterations in biomechanical changes in human gait. In literature, for the last 50 years, researchers are trying to observe the biomechanical changes due to DSPN by studying muscle electromyography (EMG), and ground reaction forces (GRF). However, the literature is contradictory. In such a scenario, we are proposing to use Machine learning techniques to identify DSPN patients by using EMG, and GRF data. We have collected a dataset consists of three lower limb muscles EMG (tibialis anterior (TA), vastus lateralis (VL), gastrocnemius medialis (GM) and 3-dimensional GRF components (GRFx, GRFy, and GRFz). Raw EMG and GRF signals were preprocessed, and a newly proposed feature extraction technique scheme from literature was applied to extract the best features from the signals. The extracted feature list was ranked using Relief feature ranking techniques, and highly correlated features were removed. We have trained different ML models to find out the best-performing model and optimized that model. We trained the optimized ML models for different combinations of muscles and GRF components features, and the performance matrix was evaluated. This study has found ensemble classifier model was performing in identifying DSPN Severity, and we optimized it before training. For EMG analysis, we have found the best accuracy of 92.89% using the Top 14 features for features from GL, VL and TA muscles combined. In the GRF analysis, the model showed 94.78% accuracy by using the Top 15 features for the feature combinations extracted from GRFx, GRFy and GRFz signals. The performance of ML-based DSPN severity classification models, improved significantly, indicating their reliability in DSPN severity classification, for biomechanical data.Comment: 17 pages, 15 figures, 8 table