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- Genre: Masters Thesis
Description
Modern communication systems call for state-of-the-art links that offer almost idealistic performance. This requirement had pushed the technological world to pursue communication in frequency bands that were almost incomprehensible back when the first series of cordless cellphones were invented. These requirements have impacted everything from civilian requirements, space, medical diagnostics to defense technologies and have ushered in a new era of advancements. This work presents a new and novel approach towards improving the conventional phased array systems. The Intelligent Phase Shifter (IPS) offers phase tracking and discrimination solutions that currently plague High-Frequency wireless systems. The proposed system is implemented on (CMOS) process node to better scalability and reduce the overall power dissipated. A tracking system can discern Radio Frequency (RF) Signals’ phase characteristics using a double-balanced mixer. A locally generated reference signal is then matched to the phase of the incoming receiver using a fully modular yet continuous complete 360ᵒ phase shifter that alters the phase of the local reference and matches the phase with that of an incoming RF reference. The tracking is generally two control voltages that carry In-phase and Quadrature-phase information. These control signals offer the capability of controlling similar devices when placed in an array and eliminating any ambiguity that might occur due to in-band interference.
ContributorsLakshminarasimhaiah Rajendra, Yashas (Author) / Zeinolabedinzadeh, Saeed (Thesis advisor) / Trichopoulos, Georgios (Committee member) / Aberle, James (Committee member) / Arizona State University (Publisher)
Created2021
Description
Visual navigation is a useful and important task for a variety of applications. As the prevalence of robots increase, there is an increasing need for energy-efficient navigation methods as well. Many aspects of efficient visual navigation algorithms have been implemented in the literature, but there is a lack of work on evaluation of the efficiency of the image sensors. In this thesis, two methods are evaluated: adaptive image sensor quantization for traditional camera pipelines as well as new event-based sensors for low-power computer vision.The first contribution in this thesis is an evaluation of performing varying levels of sensor linear and logarithmic quantization with the task of visual simultaneous localization and mapping (SLAM). This unconventional method can provide efficiency benefits with a trade off between accuracy of the task and energy-efficiency. A new sensor quantization method, gradient-based quantization, is introduced to improve the accuracy of the task. This method only lowers the bit level of parts of the image that are less likely to be important in the SLAM algorithm since lower bit levels signify better energy-efficiency, but worse task accuracy. The third contribution is an evaluation of the efficiency and accuracy of event-based camera intensity representations for the task of optical flow. The results of performing a learning based optical flow are provided for each of five different reconstruction methods along with ablation studies. Lastly, the challenges of an event feature-based SLAM system are presented with results demonstrating the necessity for high quality and high resolution event data. The work in this thesis provides studies useful for examining tradeoffs for an efficient visual navigation system with traditional and event vision sensors. The results of this thesis also provide multiple directions for future work.
ContributorsChristie, Olivia Catherine (Author) / Jayasuriya, Suren (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2022
Description
Localization tasks using two-way ranging (TWR) are making headway in modern daynavigation applications as an alternative to legacy global navigation satellite systems
(GNSS) such as GPS. There is not currently literature that provides a closed-form
expression for estimation performance bounds on position and attitude when a TWR
system is employed. A Cramer-Rao Lower Bounds (CRLB) is derived for position
and orientation estimation using both 2-D and 3-D geometries. A literature review is
performed to give background and detail on the tools needed for a thorough analysis of
this problem. Popular Least Squares techniques and solutions to Wahba’s problem are
compared to the derived bounds as proof of correctness using Monte Carlo simulations.
A brief exploration on estimation performance using an Extended Kalman Filter for
non-stationary users is also looked at as an introduction to future extensions to this
work. The literature Applications like the CHP2 system are discussed as well to show
how secure, inexpensive and robust implementation of TWR is highly feasible.
i
ContributorsWelker, Samuel (Author) / Bliss, Daniel (Thesis advisor) / Herschfelt, Andrew (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2022
Description
Nowadays, demand from the Internet of Things (IoT), automotive networking, and video applications is driving the transformation of Ethernet. It is a shift towards time-sensitive Ethernet. As a large amount of data is transmitted, many errors occur in the network. For this increased traffic, a Time Sensitive Network (TSN) is important. Time-Sensitive Network (TSN) is a technology that provides a definitive service for time sensitive traffic in an Ethernet environment that provides time-synchronization. In order to efficiently manage these errors, countermeasures against errors are required. A system that maintains its function even in the event of an internal fault or failure is called a Fault-Tolerant system. For this, after configuring the network environment using the OMNET++ program, machine learning was used to estimate the optimal alternative routing path in case an error occurred in transmission. By setting an alternate path before an error occurs, I propose a method to minimize delay and minimize data loss when an error occurs. Various methods were compared.
First, when no replication environment and secondly when ideal replication, thirdly random replication, and lastly replication using ML were tested. In these experiments, replication in an ideal environment showed the best results, which is because everything is optimal. However, except for such an
ideal environment, replication prediction using the suggested ML showed the best results.
These results suggest that the proposed method is effective, but there may be problems
with efficiency and error control, so an additional overview is provided for further
improvement.
ContributorsLee, Sang hee (Author) / Reisslein, Martin (Thesis advisor) / LiKamWa, Robert (Committee member) / Thyagaturu, Akhilesh (Committee member) / Arizona State University (Publisher)
Created2022
Description
The penetration of renewable energy in the power system has grown considerably in the past few years. While this use may come with an abundance of advantages, it also introduces new challenges in operating the 100+ years old electrical network. Fundamentally, the power system relies on a real-time balance of generation and demand. However, renewable resources such as solar and wind farms are not available throughout the day. Furthermore, they introduce temporal variability to the generation process due to metrological factors, making the balance of generation and demand precarious. Utilities use standby units with reserve power and high ramp-up, ramp-down capabilities to ensure balance. However, such solutions can be very costly. An accurate scenario generation and forecasting of the stochastic variables (load and renewable resources) can help reduce the cost of these solutions. The goal of this research is to solve the scenario generation and forecasting problems using state-of-the-art machine learning techniques and algorithms. The training database is created using publicly available data obtained from NREL and the Texas-2000 bus system. The IEEE-30 bus system is used as the test system for the analysis conducted here. The conventional generators of this system are replaced with solar farms and wind farms. The ability of four machine learning algorithms in addressing the scenario generation and forecasting problems are investigated using appropriate metrics.
The first machine learning algorithm is the convolutional neural network (CNN). It is found to be well-suited for the scenario generation problem. However, its inability to capture certain intricate details about the different variables was identified as a possible drawback. The second algorithm is the long-short term memory-variational auto-encoder (LSTM-VAE). It generated scenarios that are very similar to the actual scenarios indicating that it is suitable for solving the forecasting problem. The third algorithm is the conditional generative adversarial network (C-GAN). It was extremely effective in generating scenarios when the number of variables were small. However, its scalability was found to be a concern. The fourth algorithm is the spatio-temporal graph convolutional network (STGCN). It was found to generate representative correlated scenarios effectively.
ContributorsAlhazmi, Mohammed Ahmed (Author) / Pal, Anamitra (Thesis advisor) / Ayyanar, Raja (Committee member) / Holbert, Keith (Committee member) / Arizona State University (Publisher)
Created2021
Description
Data transmission and reception has become an important aspect in day-to-day communication. With advancement in technology, it dictates the need for accurate data transmission and reception. For this very reason, wireless transceivers are employed in almost every industrial domain for numerous applications. A special concept of distributed transceivers is proven to be extremely useful in the latest technologies like Internet of Things. As the name suggests, this is a collaborative communication technique where multiple transceivers are synchronized for faster and much more reliable communication. This imposes a major challenge while designing this kind of a transceiver, as all the transceivers should be operating with carrier synchronization to maintain the proper collaboration. While there are several ways to establish this sync, this thesis emphasizes one of those techniques and tries to resolve the issue in design. The carrier synchronization is achieved using time division synchronization technique. Several challenges in implementing this technique were addressed using various models simulated in MATLAB Simulink and Keysight ADS. An in detail analysis has been performed for all the techniques used for this implementation to provide a diverse perspective.
ContributorsBoorela, Venkata Srilekhya (Author) / Zeinolabedinzadeh, Saeed (Thesis advisor) / Trichopoulos, Georgios C. (Committee member) / Aberle, James (Committee member) / Arizona State University (Publisher)
Created2021
Description
The growth in speed and density of programmable logic devices, such as Field programmable gate arrays (FPGA), enables sophisticated designs to be created within a short time frame. The flexibility of a programmable device alleviates the difficulty of the integration of a design with a wide range of components on a single chip. FPGAs bring both performance and power efficiency, especially for compute or data-intensive applications. Efficient and accurate mRNA quantification is an essential step for molecular signature identification, disease outcome prediction, and drug development, which is a typical compute- and data-intensive compute workload. In this work, I propose to accelerate mRNA quantification with FPGA implementation. I analyze the performance of mRNA Quantification with FPGA, which shows better or similar performance compared to that of CPU implementation.
ContributorsKim, Kiju (Author) / Fan, Deliang (Thesis advisor) / Cao, Kevin (Committee member) / Zhang, Wei (Committee member) / Arizona State University (Publisher)
Created2022
Description
Brushless DC (BLDC) motors are becoming increasingly common in various industrial and commercial applications such as micromobility and robotics due to their high torque density and efficiency. A BLDC Motor is a three-phase synchronous motor that is very similar to a non-salient Permanent Magnet Synchronous Motor (PMSM) with key differences lying in the non-ideal characteristics of the motor; the most prominent of these is BLDC motors have trapezoidal-shaped Back-Electromotive Force (BEMF). Despite their advantages, a present weakness of BLDC motors is the difficulty controlling these motors at standstill and low-speed conditions that require high torque. These operating conditions are common in the target applications and almost always necessitate the use of external sensors which introduce additional costs and points of failure. As such, sensorless based methods of position estimation would serve to improve system reliability, cost, and efficiency. High Frequency (HF) pulsating voltage injection in the direct axis is a popular method of sensorless control of salient-pole Interior-mount Permanent Magnet Synchronous Motors (IPMSM); however, existing methods are not sufficiently robust for use in BLDC and small Surface-mount Permanent Magnet Synchronous Motors (SPMSM) and are accompanied by other issues, such as acoustic noise. This thesis proposes novel improvements to the method of High Frequency Voltage Injection to allow for practical use in BLDC Motors and small SPMSM. Proposed improvements include 1) a hybrid frequency generator which allows for dynamic frequency scaling to improve tracking and eliminate acoustic noise, 2) robust error calculation that is stable despite the non-ideal characteristics of BLDC Motors, 3) gain engineering of Proportional-Integral (PI) type Phase-Locked-Loop (PLL) trackers that further lend stability, 4) observer decoupling mechanism to allow for seamless transition into state-of-the-art BEMF sensing methods at high speed, and 5) saliency boosting that allows for continuous tracking of saliency under high torque load. Experimental tests with a quadrature encoder and torque efficiency calculations on a dynamometer verify the practicality of the proposed algorithm and improvements.
ContributorsYin, Kai (Author) / Vrudhula, Sarma (Thesis advisor) / Chickamenahalli, Shamala (Thesis advisor) / Pal, Anamitra (Committee member) / Arizona State University (Publisher)
Created2021
Description
In order to meet the world’s growing energy need, it is necessary to create a reliable, robust, and resilient electric power grid. One way to ensure the creation of such a grid is through the extensive use of synchrophasor technology that is based on devices called phasor measurement units (PMUs), and their derivatives, such as μPMUs. Global positioning system (GPS) time-synchronized wide-area monitoring, protection, and control enabled by PMUs has opened up new ways in which the power grid can tackle the problems it faces today. However, with implementation of new technologies comes new challenges, and one of those challenges when it comes to PMUs is the misuse of GPS as a method to obtain a time reference.The use of GPS in PMUs is very intuitive as it is a convenient method to time stamp electrical signals, which in turn helps provide an accurate snapshot of the performance of the PMU-monitored section of the grid. However, GPS is susceptible to different types of signal interruptions due to natural (such as weather) or unnatural (jamming, spoofing) causes. The focus of this thesis is on demonstrating the practical feasibility of GPS spoofing attacks on PMUs, as well as developing novel countermeasures for them.
Prior research has demonstrated that GPS spoofing attacks on PMUs can cripple power system operation. The research conducted here first provides an experimental evidence of the feasibility of such an attack using commonly available digital radios known as software defined radio (SDR). Next, it introduces a new countermeasure against such attacks using GPS signal redundancy and low power long range (LoRa) spread spectrum modulation technique. The proposed approach checks the integrity of the GPS signal at remote locations and compares the data with the PMU’s current output. This countermeasure is a steppingstone towards developing a ready-to-deploy system that can provide an instant solution to the GPS spoofing detection problem for PMUs already placed in the power grid.
ContributorsSaadedeen, Fakhri G (Author) / Pal, Anamitra (Thesis advisor) / Sankar, Lalitha (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2021
Description
The reconfigurable intelligent surface (RIS) shown in this work is a programmable metasurface integrated with a dedicated microcontroller that redirects an impinging signal to the desired direction. Its characteristic allows the RIS to act as a mirror for microwave signals. Unlike a perfect electric conductor (PEC), the RIS has much more flexibility in redirecting signals. This work involves the measurement of a passive, fixed beam, 25x32 element mmWave RIS that operates at 28.5 GHz. Bistatic and monostatic measurement setups are both used to find the radar cross section (RCS) of the RIS. The process of creating the measurement setups and the final measurement results is discussed. The measurement setup is further characterized using the High-Frequency Structure Simulator (HFSS) software and the final measurement results are compared to analytical solutions computed using MATLAB. The first prototype of the RIS has a loss of 8.4 dB when compared to a PEC and is physically curved. There is also a side lobe at the boresight of the RIS board that is only 8 dB less than the main beam in best-case scenario. This curvature causes issues with the monostatic measurement because it changes the phase that arrives at the RIS. The second prototype of the RIS has only 5.84 dB of loss compared to PEC. This measurement setup behaves mostly as expected when comparing the measurement results to the analytical solutions and given the limitations of the setup. A collimating lens was used as a part of the setup which reflects part of the incoming signal. The edge of the lens also causes diffraction. These factors contribute to multipath interference arriving at the receive antenna and increases measurement error. The lens also creates unequal amplitude illumination across the surface of the RIS which changes the RCS pattern. Using the lens allows a more space-efficient setup while still obtaining relatively constant phase illuminating across the RIS board.
ContributorsTjahjadi, Brian (Author) / Trichopoulos, Georgios C (Thesis advisor) / Aberle, James T (Committee member) / Imani, Seyedmohammadreza F (Committee member) / Arizona State University (Publisher)
Created2022