Electrical and Computer Engineering

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https://hdl.handle.net/10315/35603

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Open Access
Distance Protection Challenges of Converter-Interfaced Renewable Energy Sources
(2018-11-21) Banaiemoqadamfariman, Amin; Hooshyar, Ali
Full-scale converter-interfaced renewable energy sources (CIRESs) can cause misoperation of distance relays installed in their vicinity. Such failure stems from the different fault behavior of CIRESs compared to synchronous generators (SGs), based on which existing relays have been developed. Several measures have been devised to improve the performance of distance protection by modifying existing relays. This thesis proposes a new approach to tackle this problem. The prime objective of this method is to mimic certain features of SGs' fault current while the constraints of a converter, such as its limited fault current magnitude, are satisfied. As a result, correct operation of distance relays close to CIRESs is ensured regardless of the fault characteristics, including its type, resistance, and location. Some salient features of the proposed method are its simplicity, compatibility with off-the-shelf relays, independence from the voltage and power rating of the CIRES, using only local measurements and being cost-effective.
Open Access
An Encoder-Decoder Based Basecaller for Nanopore DNA Sequencing
(2019-07-02) Abbaszadegan, Mahdieh; Magierowski, Sebastian
Nanopore DNA sequencing is a method in which DNA bases are determined (basecalled) using electric current signals generated by passing DNA through nanopore sensors. The raw measured signals can be aggregated into event data presenting new bases entering the nanopore. This thesis has two contributions. First, we implemented RNN-based single- and double-strand basecallers for simulated event data to analyze the effect of signal noise. As the SNR decreased from 20 dB to 5 dB, the accuracy of the single-strand basecaller dropped 9% while the accuracy of double-strand basecaller only dropped 0.5%. Second, we implemented an end-to-end single-strand basecaller, directly processing the raw signal using an encoder-decoder model with attention instead of the CTC-style approach used in available basecallers. We achieved an accuracy of 81.9% for a viral sample and an accuracy of 90.9% for a bacterial sample. Our accuracy is comparable to state-of-the-art basecallers with a considerably smaller model.
Open Access
Hardware Accelerated DNA Sequencing
(2019-07-02) Wu, Zhongpan; Magierowski, Sebastian
DNA sequencing technology is quickly evolving. The latest developments ex- ploit nanopore sensing and microelectronics to realize real-time, hand-held devices. A critical limitation in these portable sequencing machines is the requirement of powerful data processing consoles, a need incompatible with portability and wide deployment. This thesis proposes a rst step towards addressing this problem, the construction of specialized computing modules { hardware accelerators { that can execute the required computations in real-time, within a small footprint, and at a fraction of the power needed by conventional computers. Such a hardware accel- erator, in FPGA form, is introduced and optimized specically for the basecalling function of the DNA sequencing pipeline. Key basecalling computations are identi- ed and ported to custom FPGA hardware. Remaining basecalling operations are maintained in a traditional CPU which maintains constant communications with its FPGA accelerator over the PCIe bus. Measured results demonstrated a 137X basecalling speed improvement over CPU-only methods while consuming 17X less power than a CPU-only method.
Open Access
Voltage and Frequency Recovery in Power System and MicroGrids Using Artificial Intelligent Algorithms
(2019-11-22) Rahmani, Soleiman; Rezaei Zare, Afshin
This thesis developed an advanced assessment tools to recover the power system voltage margin to the acceptable values during the disturbance. First, the effect of disturbance in islanded microgrids are analyzed using power factor-based power-voltage curves and a comprehensive under voltage-frequency load shedding(UVFLS) method is proposed as a last resort in order to restore the system voltage and frequency. The effect of disturbance in conventional power system is investigated by introducing a phenomenon called fault induced delayed voltage recovery(FIDVR) and comprehensive real-time FIDVR assessments are proposed to employ appropriate emergency control approaches as fast as possible to maintain the system voltage margins within the desired range. Then, polynomial regression techniques have been used for predicting the FIDVR duration. Next, advanced FIDVR assessment is implemented which simultaneously predicts whether the event can be classified as FIDVR or not and also predicts the duration of FIDVR with high accuracy.
Open Access
A Soft Switched, Single-Switch Electrolytic Capacitor-less Step-Up Converter for Photovoltaic Energy Application
(2019-11-22) Kanathipan, Kajanan; Lam, John Chi Wo
In this thesis, a single switch, electrolytic capacitor-less quasi-resonant step-up DC/DC converter is proposed for solar energy applications. The proposed converter is an improved coupled-magnetic based topology that requires only a single switch. By operating the input inductor of the proposed converter in continuous conduction mode (CCM) the required input capacitance is reduced and hence, allows for a small sized film capacitor to be used. In addition, the proposed circuit is able to achieve a large step-up gain while minimizing the ratio between the peak switch voltage and the circuit output voltage. Two different modes of operation are presented and discussed for the proposed circuit which can achieve a very large gain and a very small peak switch voltage to circuit output voltage ratio simultaneously. A maximum power point tracking controller is also developed to work with the proposed step-up DC/DC converter through the use of variable frequency control scheme. Simulation and experimental results on a proof-of-concept, 35V/380V, 100W, 100kHz, hardware prototype are provided for both modes of operation for fixed and varying light intensities to highlight the merits and performance of the proposed converter.
Open Access
Ultra High Speed Single-Ended Traveling Wave Based Protection of Transmission Lines after Auto-Reclosure
(2020-05-11) Mohammadifirozjaee, Mohammad; Hooshyar, Ali
The auto-reclosure take place after a fault in power lines, in order to remove the temporary fault and reconnect the line as fast as possible. As there is no information whether the fault is temporary or permanent, the auto-reclosure will be carried out for permanent faults too. In the latter case, reconnecting the power line can result in extensive damage and it can even increase the restoration time. As a result, putting an end to auto-reclosure on a permanent fault is really important. Considering the speed and reliability of the traveling wave protection method, this research considers it for an auto-reclosure case. Double-ended and single-ended traveling wave based protection schemes are proposed for after auto-reclosure using different parameters such as traveling waves attenuation, polarity and arrival time. Finally, extensive simulation has been carried out to verify the proposed scheme, where all the results confirm the effectiveness of the new practices.
Open Access
Power Distance Table for EV Charger Stations in Distribution System
(2020-05-11) Babaeiyazdi, Iman; Zare, Afshin Rezaei
In this thesis, the aim is to investigate the unbalanced voltage behaviour of the fast charging stations and their effects on distribution power systems. In the first stage, the fast charger is developed to derive the response of the charger to the unbalanced input voltage. This response allows us to model the charging station as a load in power flow analysis. In the next stage of the study, a simplified model is proposed to incorporate the behaviour of the fast charger in power flow analysis. Different feeders data of IEEE benchmarks such as IEEE 34-bus, 37-bus, and 123-bus are used in the base benchmark, which is IEEE 30-bus, using the proposed simplified model. Then, maximum charging capacity of the stations and unbalanced voltage ratio (UVR) is calculated for any bus of interest that the charging station has been connected to. This task is done while the system is exposed to two constraints of UVR and voltage. The power flow analysis results indicate that for the different feeders data, UVR of the system after connection of charging stations is the dominant constraint for some buses and it prevents further integration of fast charging station to the distribution system. Therefore, in order to mitigate unbalanced voltage in the system, partial transposition is utilized. In the partial transposition, the feeders are transposed and divided in two equal sections. After applying partial transposition to the feeders data, for the case of IEEE 34-bus, the UVR after connection of charging station was below the permissible value of 3%, but for IEEE 37-bus and 123-bus some buses still suffer from high UVR. Accordingly, a modified partial transposition was adopted as another alternative. The results demonstrate that the UVR of the system after applying modified partial transposition to the feeder data of IEEE 37-bus and 123-bus has decreased below the standard value of 3% and the system can accommodate higher capacity of fast charging stations. Finally, according to the power flow analysis a power distance table is acquired for the feeders data that predicts the maximum charging capacity that can be connected to the system based on its distance from the main source without violating the systems operational constraint.
Open Access
Fully-Implantable Self-Contained Dual-Channel Electrical Recording and Directivity-Enhanced Optical Stimulation System on a Chip
(2020-05-11) Yousefi, Tayebeh; Kassiri, Hossein
This thesis presents an integrated system-on-a-chip (SoC), designed, fabricated, and characterized for conducting simultaneous dual-channel optogenetic stimulation and electrophysiological recording. An inductive coil as well as power management circuits are also integrated on the chip, enabling wireless power reception, hence, allowing full implantation. The optical stimulation channels host a novel LED driver circuit that can generate currents up to 10mA with a minimum required headroom voltage reported in the literature, resulting in a superior power efficiency compared to the state of the art. The output current in each channel can be programmed to have an arbitrary waveform with digitally-controlled magnitude and timing. The final design is fabricated as a 34 mm2 microchip using a CMOS 130nm technology and characterized both in terms of electrical and optical performance. A pair of custom-designed inkjet-printed micro-lenses are also fabricated and placed on top of the LEDs. The lenses are optimized to enhance the light directivity of optical stimulation, resulting in significant improvements in terms of spatial resolution, power consumption (30.5x reduction), and safety aspects (temperature increase of <0.1c) of the device.
Open Access
Digital Sun Sensor Design for Nanosatellite Applications
(2020-08-11) Bolshakov, Konstantin; Lee, Regina S. K.
In this research a novel, semi-custom design of a Sun sensor, based on the orthogonal photodiode array design approach, applicable for nanosatellites and other small spacecraft is proposed. A common and well-known strategy of application of a geometrical aperture mask on the light detectors is improved upon and utilised in a non-conventional fashion in the design presented in this thesis. The main characteristic of this design, that is investigated in this work, is the inclusion of a chirped pattern of slits, while using a digital readout of the photodiode arrays. This pattern is expected to allow greater angle detection accuracy while digital photodiode readout decreases complexity and power consumption. The array based design approach is chosen due to lower power requirements, mass savings and simpler readout interface and signal processing in comparison to the matrix based approach. The Sun sensor design presented maximises the accuracy, while keeping the cost, development and implementation time and complexity to minimum. The design will be demonstrated on DESCENT mission CubeSat as a part of the Moth-Eye Anti-Reflective solar cell coating payload. The characterisation of the non-calibrated manufactured part shows that the sensor is capable of 1 degree accuracy, which is expected to improve to under 0.5 degrees with calibration and sensor data processing.
Open Access
A Mega-Hertz Micro Converter with Extended Soft Switching Operation for Photovoltaic (PV) Application
(2020-08-11) Ahmadiankalati, Samira; Lam, John Chi Wo
The increasing greenhouse effect and relative environmental pollution, along with limited fossil fuel has made it urgent to a transition towards renewable energy sources. The combined global capacity of Photovoltaic (PV) energy has increased considerably from 6.01 gigawatts (GW) to 505 GW from 2006 to 2018. A typical power configuration of a PV energy conversion system consists of a front-end DC/DC micro converter that is used to provide maximum power point tracking (MPPT), as well as to provide some step-up voltage conversion from the output of the PV solar panel. Different DC/DC PV power converters have been reported in literature. The existing DC/DC converters either require a high number of switches and magnetic components, suffer from high voltage stress over some circuit elements, or have low circuit efficiency and restricted switching frequency due to hard switching (hence large size passive components are required). In this thesis, a very high frequency DC/DC micro converter with inherent extended soft-switching operation is proposed for PV energy conversion systems. In the proposed topology, a boost-based MPPT circuit is integrated with a CL (capacitor-inductor) parallel resonant converter to form a single stage DC/DC PV micro converter. While the proposed converter has an auxiliary circuit to assist extended soft- switching operation, the inductor in the auxiliary circuit is coupled with the boost inductor so that the size and space of the overall circuit can be further reduced. A modified enhanced maximum power point tracking algorithm is also developed to work with the proposed step-up DC/DC micro converter. The theoretical analysis and the operating principles of the proposed converter will be discussed in this thesis. Simulation and experimental results on a MHz (Mega-Hertz) proof-of-concept hardware prototype are provided to highlight the performance of the proposed circuit.
Open Access
Assisted Target Detection in Airborne Search and Rescue
(2020-11-13) Taheri-Shirazi, Maryam; Elder, James H.
Finding and rescuing people from downed aircraft is challenging in many parts of the world, including Canada. Because the Canadian military still relies on the naked eye to conduct searches, airborne search and rescue could benefit greatly from advanced sensor systems. Partial automation of target detection could alleviate operator workload and potentially improve rescue efforts. One of the obstacles to developing such a system has been the lack of a large, realistic, and ground-truthed search and rescue (SAR) dataset. I used a new dataset for airborne SAR collected in 2014 by the National Research Council Flight Research Laboratory (NRC-FRL) and labeled approximately 40,000 frames, to extract roughly 20,000 negative and 20,000 positive images. Then I tested three ATD methods on this dataset in order to develop more advanced assisted target detection algorithms for thermal infrared (IR) images.
Open Access
Incorporated Temporal Action Proposal Generation
(2020-11-13) Sanu, Joseph; Jiang, Hui
We propose a new unified approach to generate high quality temporal action proposals from untrimmed videos called Incorporated temporal action proposal generation. The concept behind this model is to consolidate the processes that classify the small temporal segments and evaluate the larger proposal features. In doing so, we seek to reduce the total number of computational units necessary for end-to-end proposal generation. For our research, we have conducted our experiments on the action proposal task in the ActivityNet challenge where the goal is to produce a set of candidate temporal segments that are likely to contain a human action. In addendum to the aforementioned research, we also propose an extension to this work by applying video level classification on our proposals. For this work, we emphasize the importance of accurate sequential modelling of temporal segments to properly distinguish between macro and micro-level actions within untrimmed videos and we compare our results with other state-of-the-art spatio-temporal models.
Open Access
A Spherical Visually-Guided Robot
(2020-11-13) Dey, Bir Bikram; Jenkin, Michael R.
Spherical robots provide a number of advantages over their wheeled counterparts, but they also presents a number of challenges and complexities. Chief among these are issues related to locomotive strategies and sensor placement and processing given the rolling nature of the device. Here we describe Dragon Ball, a visually tele-operated spherical robot. The Dragon Ball utilizes a combination of a geared wheel to move the center of mass of the vehicle coupled with a torque wheel to change direction. Wide angled cameras mounted on the robot's horizontal axis provide a 360 view of the space around the robot and are used to simulate a traditional pan tilt zoom camera mounted on the vehicle for visual tele-operation. The resulting vehicle is well suited for deployment in contaminated environments for which vehicle remediation is a key operational requirement.
Open Access
An 8-Channel Bidirectional Neurostimulator IC with a Highly-Linear High-Dynamic-Range ADC-Direct Architecture for Simultaneous Recording and Stimulation
(2021-03-08) Moeinfard, Tania; Kassiri, Hossein
This thesis presents the design, implementation, and validation of an 8-channel bidirectional neurostimulator IC with a highly-linear high-dynamic-range ADC-direct architecture for simultaneous recording and stimulation. Each channel hosts a novel highly-linear high-dynamic-range recording architecture capable of amplification and quantization of brains neural signals in the presence of large differential-mode and common-mode stimulation artifacts, as well as a fully-programmable 8-bit current-mode electrical stimulator. The architecture enables the possibility of a patient-specific stimulation therapy required for the next generation of implantable closed-loop neuro-stimulators used for treatment of various neurological disorders. The proposed design adopts an ADC-direct architecture employing a dual-loop SAR-assisted continuous-time delta-sigma ADC architecture for differential-mode stimulation artifacts and offset removal. The presented channel achieves a high input impedance (1.8 G at 1 kHz), 400 mV linear input signal range, 94 dB dynamic range, and consumes 4.6 W with a signal bandwidth of 5 kHz.
Open Access
An Exploratory Study on the Platforms of Sharing Reusable Machine Learning Models
(2021-03-08) Xiu, Minke; Jiang, ZhenMing "Jack"
Recent advances in Artificial Intelligence, especially in Machine Learning (ML), have brought applications previously considered as science fiction (e.g., virtual personal assistants and autonomous cars) into the reach of millions of everyday users. Since modern ML technologies like deep learning require considerable technical expertise and resource to build custom models, reusing existing models trained by experts has become essential. Currently the ML models are shared, distributed, or retailed on multiple ML model platforms which can be divided into two categories based on their usage patterns: (1) ML model stores whose models can be deployed and served with the help of cloud infrastructure, and (2) ML package repositories whose models are free but need to be deployed and used (e.g., embedded into users applications as a software component) manually. We conducted an exploratory study on the above two categories of ML model platforms: ML model stores and ML package repositories. We analyzed the structure and the contents of the ML models platforms, as well as functionalities provided by the package managers. The research subjects were three general purpose ML model stores (AWS marketplace, ModelDepot, and Wolfram neural net repository) and two popular ML package repositories (TensorFlow Hub and PyTorch Hub). When studying the structure of ML model platforms and functionalities of package managers, we compared them against their counterparts from traditional software development: ML model stores vs. mobile app stores (e.g., Google Play and Apple App Store), and ML package repositories vs. programming language package repositories (e.g., npm, PyPI, and CRAN). Through our study, we identified special software engineering practices and challenges for sharing, distributing, and retailing ML models. The implications from this thesis will be helpful for stakeholders to make the ML model platforms better serve the users (i.e., software engineers, data scientists and researchers).
Open Access
Stochastic Geometry for Mobility-Aware Performance Modeling in 6G Multi-band Wireless Networks
(2021-07-06) Hossan, Md Tanvir; Tabassum, Hina
Using tools from stochastic geometry, I develop a stochastic geometry-based tractable framework to analyze the performance of a mobile user in a two-tier wireless network operating on sub-6GHz and terahertz (THz) transmission frequencies. Specifically, using an equivalence distance approach, I characterize the overall handoff (HO) rate in terms of the horizontal and vertical HO probability. In addition, I characterize novel coverage probability expressions for THz network in the presence of molecular absorption noise and highlight its significant impact on the users' performance. Specifically, I derive a novel closed-form expression for the Laplace Transform of the cumulative interference in the presence of molecular noise observed by a mobile user in a hybrid RF-THz network. Furthermore, I provide a novel approach to derive the conditional distance distributions of a typical user in a hybrid RF-THz network. Finally, using the overall HO rate and coverage probability expressions, the mobility-aware probability of coverage has been derived in a hybrid RF-THz network. The mathematical results validate the correctness of the derived expressions using Monte-Carlo simulations. The results offer insights into the adverse impact of users' mobility and molecular noise in THz transmissions on the probability of coverage of mobile users. The results demonstrate that a small increase in the intensity of THz base-station (TBSs) (about 5 times) can increase the HO probability much more compared to the case when the intensity of RF base-station (RBSs) is increased by 100 times. Furthermore, I note that high molecular absorption can be beneficial (in terms of minimizing interference) for dense deployment of TBSs and the benefits can outweigh the drawbacks of signal degradation due to molecular absorption.
Open Access
An Efficient Machine Learning Software Architecture for Internet of Things
(2021-07-06) Chaudhary, Mahima; Litoiu, Marin
Internet of Things (IoT) software is becoming a critical infrastructure for many domains. In IoT, sensors monitor their environment and transfer readings to cloud, where Machine Learning (ML) provides insights to decision-makers. In the healthcare domain, the IoT software designers have to consider privacy, real-time performance and cost in addition to ML accuracy. We propose an architecture that decomposes the ML lifecycle into components for deployment on a two-tier cloud, edge-core. It enables IoT time-series data to be consumed by ML models on edge-core infrastructure, with pipeline elements deployed on any tier, dynamically. The architecture feasibility and ML accuracy are validated with three brain-computer interfaces (BCI) based use-cases. The contributions are two-fold: first, we propose a novel ML-IoT pipeline software architecture that encompasses essential components from data ingestion to runtime use of ML models; second, we assess the software on cognitive applications and achieve promising results in comparison to literature.
Open Access
Resource Allocation in Multi-access Edge Computing (MEC) Systems: Optimization and Machine Learning Algorithms
(2021-07-06) Zarandi, Sheyda; Tabassum, Hina
With the rapid proliferation of diverse wireless applications, the next generation of wireless networks are required to meet diverse quality of service (QoS) in various applications. The existing one-size-fits-all resource allocation algorithms will not be able to sustain the sheer need of supporting diverse QoS requirements. In this context, radio access network (RAN) slicing has been recently emerged as a promising approach to virtualize networks resources and create multiple logical network slices on a common physical infrastructure. Each slice can then be tailored to a specific application with distinct QoS requirement. This would considerably reduce the cost of infrastructure providers. However, efficient virtualized network slicing is only feasible if network resources are efficiently monitored and allocated. In the first part of this thesis, leveraging on tools from fractional programming and Augmented Lagrange method, I propose an efficient algorithm to jointly optimize users offloading decisions, communication, and computing resource allocation in a sliced multi-cell multi-access edge computing (MEC) network in the presence of interference. The objective is to minimize the weighted sum of the delay deviation observed at each slice from its corresponding delay requirement. The considered problem enables slice prioritization, cooperation among MEC servers, and partial offloading to multiple MEC servers. On another note, due to high computation and time complexity, traditional centralized optimization solutions are often rendered impractical and non-scalable for real-time resource allocation purposes. Thus, the need of machine learning algorithms has become more vital than ever before. To address this issue, in the second part of this thesis, exploiting the power of federated learning (FDL) and optimization theory, I develop a federated deep reinforcement learning framework for joint offloading decision and resource allocation in order to minimize the joint delay and energy consumption in a MEC-enabled internet-of-things (IoT) network with QoS constraints. The proposed algorithm is applied to an IoT network, since the IoT devices suffer significantly from limited computation and battery capacity. The proposed algorithm is distributed in nature, exploit cooperation among devices, preserves the privacy, and is executable on resource-limited cellular or IoT devices.
Open Access
Thermoreflectance for Contactless Sintering Characterization: From Metal Nanoparticles to Stretchable Conductors
(2021-07-06) Rahman, Md Saifur; Grau, Gerd
Sintering metal nanoparticles is a crucial step to achieve printed conductors. It is important to characterize and monitor nanoparticle sintering for process optimization and control. Here, we demonstrate that frequency-domain thermoreflectance (FDTR), an optical pump-probe technique, can be used for non-contact, non-destructive process monitoring that is compatible with high-throughput printed electronics manufacturing, unlike traditional electrical resistance measurements. The thermal conductivity measured from FDTR agrees well with thermal conductivity calculated using Wiedemann-Franz law from electrical conductivity measurements. Measurement time is reduced to 12 s by choosing a small number of measurement frequencies instead of a full frequency sweep and measuring them simultaneously. A Monte Carlo simulation was performed to predict the possibility of further reducing measurement time. Understanding of the sintering process allows tailoring of materials properties as demonstrated here to create a novel stretchable conductor. Differently sintered layers are combined to achieve a desirable stretchability-conductivity profile.
Open Access
Unrolling of Graph Total Variation for Image Denoising
(2021-07-06) Vu Huy, Duc; Cheung, Gene
While deep learning have enabled effective solutions in image denoising, in general their implementations overly rely on training data and require tuning of a large parameter set. In this thesis, a hybrid design that combines graph signal filtering with feature learning is proposed. It utilizes interpretable analytical low-pass graph filters and employs 80\% fewer parameters than a state-of-the-art DL denoising scheme called DnCNN. Specifically, to construct a graph for graph spectral filtering, a CNN is used to learn features per pixel, then feature distances are computed to establish edge weights. Given a constructed graph, a convex optimization problem for denoising using a graph total variation prior is formulated. Its solution is interpreted in an iterative procedure as a graph low-pass filter with an analytical frequency response. For fast implementation, this response is realized by Lanczos approximation. This method outperformed DnCNN by up to 3dB in PSNR in statistical mistmatch case.