Pusat Pengajian Kejuruteraaan Elektrik dan Elektronik - Tesis

Browse

Now showing 1 - 5 of 491

Evaluation of uav-based lora wireless communications under various scenarios
(2023-05-01)
Teoh Theng Kah
This research evaluates the performance of UAV (Unmanned Aerial Vehicle)-based and Non-UAV-based LoRa wireless communication under various scenario. LoRa is a long-range, low-power wireless communication technology that has become increasingly popular for IOT (Internet of Things) and other applications, due to its long-range capabilities, low power consumption, and low cost. In this study, the LoRa technology is used to establish wireless communication between a ground-based gateway (Receiver) and a moving UAV-based or Non-UAV-based end-node (Transmitter) that tested within 1 kilometer scope, for the purpose of evaluating the functionality and the signal disturbance of LoRa for both indoor and outdoor. The performance of the communication is evaluated under different scenarios, including varying distances between the gateway and the end-node, different environments (indoor and outdoor), different antenna types and battery power consumption upon data transmission. The main performance metrics that used in the evaluation are the RSSI (Received Signal Strength Indicator) and PRR (Packet Reception Rate). The results of the evaluation show that the UAV-based and Non-UAV-based LoRa technology can provide reliable and efficient wireless communication and optimum battery power consumption of the data transmission setting, with good performance tested within 1 kilometer distance and in various environments. The findings of this study can provide useful insights and guidance for the design and implementation of LoRa-based wireless communication systems for UAV applications.
Visual stimuli-based dynamic commands with single channel electroencephalography for reactive brain-computer interface applications
(2023-05-01)
Teo Jia Hui
The brain-computer interface (BCI) technology is widely used to control robotic devices using electroencephalogram (EEG) signals. Normal eye blinks are usually treated as artifacts within the recorded EEG signals but voluntary eye blinks which will cause distinct signal deflections can be introduced into BCI applications as control commands. This study designed a new reactive BCI paradigm using single-channel EEG device which combined visual stimuli and voluntary eye blinks to work on motorized actuators with different speed profiles. This proposal consisted of an EEG decoder that applied on machine learning methods to improve the accuracy of the system. Multilayer perceptron (MLP) neural network and Gaussian Process model (GPM) had been proposed to minimize the mismatches between required and actual transmitted commands. Results from thirty subjects showed that the GPM could achieve the highest 90% accuracy and lowest 1.51cm/s mean absolute error whereas MLP showed slightly better performance (86% accuracy and 1.76cm/s mean absolute error) than other comparing methods. The implementation of Hanning filter showed improvement to minimize unwanted errors in the system. Female-based model could perform slightly better than generic model on female-only test data set whereas male-based model performed similarly as generic model on male-only test data set. While other learning models were susceptible to different types of stimuli, the proposed GPM could perform consistently to demonstrate its high generalization and make it a more suitable option over MLP and other methods on such BCI system.
Asymmetric cascade face detector on multi-camera images
(2023-09-01)
Sirajdin Olagoke Adeshina
Classroom attendance is one of the measures that ensure students’ presence and punctuality in day-to-day classroom activities. Researchers emphasize its impact on the academic performance of students. The traditional method of carrying out this measure is of no standard in today’s classroom attendance system because of population, time, sophistication, exhaustiveness, classroom size, and manipulative influence. The conventional automated classroom attendance systems are designed for small classrooms using a single camera. Therefore, a design and implementation of a multicamera setup for a large lecture theatre attendance system is considered. the USM DK5 lecture theatre containing 14 volunteered participants (13 males and a female) of different races was used for the exercise. A move-shift seating arrangement, producing a total of 62 multiple-face datasets was obtained which was used for the evaluation of the proposed algorithm. An asymmetric cascade face detector was trained using faces and non-face samples. Selected Haar-like features were used based on their attention focus, area and width-to-height ratio, and feature specialization to train the algorithm. The asymmetric goal is set to minimize the False Rejection Rate (FRR) relative to the False Acceptance Rate (FAR). A relationship between the two factors (FRR and FAR) was established using a constant (l) as a trade-off between the two factors for automatic adjustment during training. Consequently, a TPR comparison of the proposed approach with the state-of-the-art tiny face (ResNet101) deep learning algorithm on images captured in USM (DK5) lecture theatre produced 96.3% to 99.6%, with an overhead constraint of 22.6s to 650.3s respectively. Tiny face as a deep learning approach with high level of accuracy, its limitations lie on the training data required, computational complexities, and implementation on the resource constraint platforms. Based on the experimental results on classroom datasets, the proposed approach shows an improvement of 8% TPR (output result of low FRR) and 7% minimization of the FRR. The average learning speed of the proposed approach was improved with 1.02s execution time per image as compared to 2.38s of the original algorithm.
Development of wearable patch antenna for 5g electronics applications by using a conductive paste based on polymer composite
(2023-08-01)
Shakhirul Bin Mat Salleh
The utilization of the 5G-specific 3.5 GHz frequency band for wireless communication integration serves as a catalyst in driving advancements within Internet of Things (IoT) technologies. Wearable antennas operating at 3.5 GHz serve as conduits to transmit vital patient health data directly to hospital health monitoring systems. However, the limitations of conventional antennas are marked by their rigidity, while textile antennas offer flexibility, their stretchability remains constrained. This challenge is addressed through the selection of Polydimethylsiloxane (PDMS) material and the formulation of a high-conductivity silver Ag-PDMS composite, boasting a conductivity of 6.58 x 106 S/m. Antenna A is designed using a higher Ag-silver conductive content (65 wt%), resulting in a measured bandwidth of 196 MHz and a gain of 2.61 dBi at 3.5 GHz, while retaining the ability to stretch up to 10%. Subsequently, Antenna A undergoes iterative enhancement as slits are introduced at each patch edge, creating an air gap substrate, and the integration of a sawtooth partial ground and reflector at the back forms Antenna B. This evolution yields measurable improvements, expanding the bandwidth 12.24% (220 MHz) and the gain 144.98% (6.38 dBi), with a stretchability of 20%. Both antennas are subjected to rigorous bending and stretching analyses to elucidate their performance when attached to the human body. Additionally, simulated SAR values for Antenna A and B remain within prescribed limits (1.6 W/kg for 1g tissues and 2 W/kg for 10g tissues) for human exposure to electromagnetic frequencies.
Microwave imaging systems using dasweighted phase coherent factor for breast cancer detection
(2023-04-01)
Rasammal A/P Rasappan
This thesis describes an improved radar based imaging systemfor breast cancer detection that employs 32 array p-slot ultra-wideband antennae. There are numerous algorithms utilized in beamforming UltraWide-Band Imaging (UWBI) system and the array processing algorithms is the one that focuses or steers the array in a particular direction. Several popular algorithms utilized for beamforming UWBI systems include DAS, DMAS, IDAS, CF-DAS, CR-DAS and RCB. Despite the latest advancement in UWBI technology, a microwave imaging system is still subjected to a limitation due to breast heterogeneity. UWBI method for breast cancer is based on the significant contrast between the dielectric properties of the healthy and malignant tissues, however recent studies concluded the contrast has dropped by 10% in heterogeneity breast. In UWBI, limited resolution and cluttering and side lobes further complicate the problem. The wavelength, λ for microwaves lies almost in the same order with the length of the target of interest. Therefore, employing higher frequencies to obtain better resolutions and improved imaging accuracy remains a challenge in microwave imaging. The heavy cluttering due to heterogeneity of the breast and the penetration depth further worsen the UWBI system. The side lobes and clutters affect the quality of the images in terms of the resolution and sub-clutter visibility. Therefore, this study attempts to address the problems by (i) to investigate an element positions of a 3D antenna array to locate and differentiate tumours with higher precision in heterogeneity breast, (ii) propose a side lobe suppression technique for the conventional DAS beamformer in order to enhance the resolution and image quality and finally, (iii) validate and evaluate the performance of the proposed algorithm using simulated and experimental data. The new reconstruction approach adds the Phase Coherence Factor (PCF) into the traditional delay and sum (DAS) beamforming algorithm, substantially eliminating side- and grating-lobe interference noise. Several breast models made from chemical mixtures generated on the basis of realistic human tissues are used to evaluate the system. Each model is housed in a hemispherical breast radome made of polylactide and encircled by 32 p-slot antennae arranged in four concentric layers. Two 16-channel multiplexers connect these antennae to an 8.5 GHz vector network analyser, automatically switching different combinations of transmitter and receiver pairs in a sequential way. With an average signal-to-clutter ratio of 7.0 dB and a full-width half-maximum of 2.3 mm, the system can accurately detect 5 mm tumours in a complicated and homogeneously dense 3D breast model. As part of future research, this study lays the path for a clinical trial involving human subjects.

Browse

Recent Submissions