Matching Items (20)
Filtering by
- All Subjects: engineering
- Genre: Masters Thesis
- Creators: Bakkaloglu, Bertan
- Creators: Redkar, Sangram
- Status: Published
Description
ABSTRACT To meet stringent market demands, manufacturers must produce Radio Frequency (RF) transceivers that provide wireless communication between electronic components used in consumer products at extremely low cost. Semiconductor manufacturers are in a steady race to increase integration levels through advanced system-on-chip (SoC) technology. The testing costs of these devices tend to increase with higher integration levels. As the integration levels increase and the devices get faster, the need for high-calibre low cost test equipment become highly dominant. However testing the overall system becomes harder and more expensive. Traditionally, the transceiver system is tested in two steps utilizing high-calibre RF instrumentation and mixed-signal testers, with separate measurement setups for transmitter and receiver paths. Impairments in the RF front-end, such as the I/Q gain and phase imbalance and nonlinearity, severely affect the performance of the device. The transceiver needs to be characterized in terms of these impairments in order to guarantee good performance and specification requirements. The motivation factor for this thesis is to come up with a low cost and computationally simple extraction technique of these impairments. In the proposed extraction technique, the mapping between transmitter input signals and receiver output signals are used to extract the impairment and nonlinearity parameters. This is done with the help of detailed mathematical modeling of the transceiver. While the overall behavior is nonlinear, both linear and nonlinear models to be used under different test setups are developed. A two step extraction technique has been proposed in this work. The extraction of system parameters is performed by using the mathematical model developed along with a genetic algorithm implemented in MATLAB. The technique yields good extraction results with reasonable error. It uses simple mathematical operation which makes the extraction fast and computationally simple when compared to other existing techniques such as traditional two step dedicated approach, Nonlinear Solver (NLS) approach, etc. It employs frequency domain analysis of low frequency input and output signals, over cumbersome time domain computations. Thus a test method, including detailed behavioral modeling of the transceiver, appropriate test signal design along with a simple algorithm for extraction is presented.
ContributorsSreenivassan, Aiswariya (Author) / Ozev, Sule (Thesis advisor) / Kiaei, Sayfe (Committee member) / Bakkaloglu, Bertan (Committee member) / Arizona State University (Publisher)
Created2011
Description
The objective of this work is to develop a Stop-Rotor Multimode UAV. This UAV is capable of vertical take-off and landing like a helicopter and can convert from a helicopter mode to an airplane mode in mid-flight. Thus, this UAV can hover as a helicopter and achieve high mission range of an airplane. The stop-rotor concept implies that in mid-flight the lift generating helicopter rotor stops and rotates the blades into airplane wings. The thrust in airplane mode is then provided by a pusher propeller. The aircraft configuration presents unique challenges in flight dynamics, modeling and control. In this thesis a mathematical model along with the design and simulations of a hover control will be presented. In addition, the discussion of the performance in fixed-wing flight, and the autopilot architecture of the UAV will be presented. Also presented, are some experimental "conversion" results where the Stop-Rotor aircraft was dropped from a hot air balloon and performed a successful conversion from helicopter to airplane mode.
ContributorsVargas-Clara, Alvaro (Author) / Redkar, Sangram (Thesis advisor) / Macia, Narciso (Committee member) / Rajadas, John (Committee member) / Arizona State University (Publisher)
Created2011
Description
The partially-depleted (PD) silicon Metal Semiconductor Field Effect Transistor (MESFET) is becoming more and more attractive for analog and RF applications due to its high breakdown voltage. Compared to conventional CMOS high voltage transistors, the silicon MESFET can be fabricated in commercial standard Silicon-on-Insulator (SOI) CMOS foundries without any change to the process. The transition frequency of the device is demonstrated to be 45GHz, which makes the MESFET suitable for applications in high power RF power amplifier designs. Also, high breakdown voltage and low turn-on resistance make it the ideal choice for switches in the switching regulator designs. One of the anticipated applications of the MESFET is for the pass device for a low dropout linear regulator. Conventional NMOS and PMOS linear regulators suffer from high dropout voltage, low bandwidth and poor stability issues. In contrast, the N-MESFET pass transistor can provide an ultra-low dropout voltage and high bandwidth without the need for an external compensation capacitor to ensure stability. In this thesis, the design theory and problems of the conventional linear regulators are discussed. N-MESFET low dropout regulators are evaluated and characterized. The error amplifier used a folded cascode architecture with gain boosting. The source follower topology is utilized as the buffer to sink the gate leakage current from the MESFET. A shunt-feedback transistor is added to reduce the output impedance and provide the current adaptively. Measurement results show that the dropout voltage is less than 150 mV for a 1A load current at 1.8V output. Radiation measurements were done for discrete MESFET and fully integrated LDO regulators, which demonstrate their radiation tolerance ability for aerospace applications.
ContributorsChen, Bo (Author) / Thornton, Trevor (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2013
Description
The objective of this thesis was to compare various approaches for classification of the `good' and `bad' parts via non-destructive resonance testing methods by collecting and analyzing experimental data in the frequency and time domains. A Laser Scanning Vibrometer was employed to measure vibrations samples in order to determine the spectral characteristics such as natural frequencies and amplitudes. Statistical pattern recognition tools such as Hilbert Huang, Fisher's Discriminant, and Neural Network were used to identify and classify the unknown samples whether they are defective or not. In this work, a Finite Element Analysis software packages (ANSYS 13.0 and NASTRAN NX8.0) was used to obtain estimates of resonance frequencies in `good' and `bad' samples. Furthermore, a system identification approach was used to generate Auto-Regressive-Moving Average with exogenous component, Box-Jenkins, and Output Error models from experimental data that can be used for classification
ContributorsJameel, Osama (Author) / Redkar, Sangram (Thesis advisor) / Arizona State University (Publisher)
Created2013
Description
The smart grid initiative is the impetus behind changes that are expected to culminate into an enhanced distribution system with the communication and control infrastructure to support advanced distribution system applications and resources such as distributed generation, energy storage systems, and price responsive loads. This research proposes a distribution-class analog of the transmission LMP (DLMP) as an enabler of the advanced applications of the enhanced distribution system. The DLMP is envisioned as a control signal that can incentivize distribution system resources to behave optimally in a manner that benefits economic efficiency and system reliability and that can optimally couple the transmission and the distribution systems. The DLMP is calculated from a two-stage optimization problem; a transmission system OPF and a distribution system OPF. An iterative framework that ensures accurate representation of the distribution system's price sensitive resources for the transmission system problem and vice versa is developed and its convergence problem is discussed. As part of the DLMP calculation framework, a DCOPF formulation that endogenously captures the effect of real power losses is discussed. The formulation uses piecewise linear functions to approximate losses. This thesis explores, with theoretical proofs, the breakdown of the loss approximation technique when non-positive DLMPs/LMPs occur and discusses a mixed integer linear programming formulation that corrects the breakdown. The DLMP is numerically illustrated in traditional and enhanced distribution systems and its superiority to contemporary pricing mechanisms is demonstrated using price responsive loads. Results show that the impact of the inaccuracy of contemporary pricing schemes becomes significant as flexible resources increase. At high elasticity, aggregate load consumption deviated from the optimal consumption by up to about 45 percent when using a flat or time-of-use rate. Individual load consumption deviated by up to 25 percent when using a real-time price. The superiority of the DLMP is more pronounced when important distribution network conditions are not reflected by contemporary prices. The individual load consumption incentivized by the real-time price deviated by up to 90 percent from the optimal consumption in a congested distribution network. While the DLMP internalizes congestion management, the consumption incentivized by the real-time price caused overloads.
ContributorsAkinbode, Oluwaseyi Wemimo (Author) / Hedman, Kory W (Thesis advisor) / Heydt, Gerald T (Committee member) / Zhang, Muhong (Committee member) / Arizona State University (Publisher)
Created2013
Description
ABSTRACT Ongoing research into wireless transceivers in the 60 GHz band is required to address the demand for high data rate communications systems at a frequency where signal propagation is challenging even over short ranges. This thesis proposes a mixer architecture in Complementary Metal Oxide Semiconductor (CMOS) technology that uses a voltage controlled oscillator (VCO) operating at a fractional multiple of the desired output signal. The proposed topology is different from conventional subharmonic mixing in that the oscillator phase generation circuitry usually required for such a circuit is unnecessary. Analysis and simulations are performed on the proposed mixer circuit in an IBM 90 nm RF process on a 1.2 V supply. A typical RF transmitter system is considered in determining the block requirements needed for the mixer to meet the IEEE 802.11ad 60 GHz Draft Physical Layer Specification. The proposed circuit has a conversion loss of 21 dB at 60 GHz with a 5 dBm LO power at 20 GHz. Input-referred third-order intercept point (IIP3) is 2.93 dBm. The gain and linearity of the proposed mixer are sufficient for Orthogonal Frequency Division Multiplexing (OFDM) modulation at 60 GHz with a transmitted data rate of over 4 Gbps.
ContributorsMartino, Todd Jeffrey (Author) / Kiaei, Sayfe (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Aberle, James T., 1961- (Committee member) / Arizona State University (Publisher)
Created2010
Description
Suction stabilized floats have been implemented into a variety of applications such as supporting wind turbines in off-shore wind farms and for stabilizing cargo ships. This thesis proposes an alternative use for the technology in creating a system of suction stabilized floats equipped with real time location modules to help first responders establish a localized coordinate system to assist in rescues. The floats create a stabilized platform for each anchor module due to the inverse slack tank effect established by the inner water chamber. The design of the float has also been proven to be stable in most cases of amplitudes and frequencies ranging from 0 to 100 except for when the frequency ranges from 23 to 60 Hz for almost all values of the amplitude. The modules in the system form a coordinate grid based off the anchors that can track the location of a tag module within the range of the system using ultra-wideband communications. This method of location identification allows responders to use the system in GPS denied environments. The system can be accessed through an Android app with Bluetooth communications in close ranges or through internet of things (IoT) using a module as a listener, a Raspberry Pi and an internet source. The system has proven to identify the location of the tag in moderate ranges with an approximate accuracy of the tag location being 15 cm.
ContributorsDye, Michaela (Author) / Redkar, Sangram (Thesis advisor) / Sugar, Thomas (Committee member) / Rogers, Bradley (Committee member) / Arizona State University (Publisher)
Created2020
Description
Switching surges are a common type of phenomenon that occur on any sort of power system network. These are more pronounced on long transmission lines and in high voltage substations. The problem with switching surges is encountered when a lot of power is transmitted across a transmission line
etwork, typically from a concentrated generation node to a concentrated load. The problem becomes significantly worse when the transmission line is long and when the voltage levels are high, typically above 400 kV. These overvoltage transients occur following any type of switching action such as breaker operation, fault occurrence/clearance and energization, and they pose a very real danger to weakly interconnected systems. At EHV levels, the insulation coordination of such lines is mainly dictated by the peak level of switching surges, the most dangerous of which include three phase line energization and single-phase reclosing. Switching surges can depend on a number of independent and inter-dependent factors like voltage level, line length, tower construction, location along the line, and presence of other equipment like shunt/series reactors and capacitors.
This project discusses the approaches taken and methods applied to observe and tackle the problems associated with switching surges on a long transmission line. A detailed discussion pertaining to different aspects of switching surges and their effects is presented with results from various studies published in IEEE journals and conference papers. Then a series of simulations are presented to determine an arrangement of substation equipment with respect to incoming transmission lines; that correspond to the lowest surge levels at that substation.
etwork, typically from a concentrated generation node to a concentrated load. The problem becomes significantly worse when the transmission line is long and when the voltage levels are high, typically above 400 kV. These overvoltage transients occur following any type of switching action such as breaker operation, fault occurrence/clearance and energization, and they pose a very real danger to weakly interconnected systems. At EHV levels, the insulation coordination of such lines is mainly dictated by the peak level of switching surges, the most dangerous of which include three phase line energization and single-phase reclosing. Switching surges can depend on a number of independent and inter-dependent factors like voltage level, line length, tower construction, location along the line, and presence of other equipment like shunt/series reactors and capacitors.
This project discusses the approaches taken and methods applied to observe and tackle the problems associated with switching surges on a long transmission line. A detailed discussion pertaining to different aspects of switching surges and their effects is presented with results from various studies published in IEEE journals and conference papers. Then a series of simulations are presented to determine an arrangement of substation equipment with respect to incoming transmission lines; that correspond to the lowest surge levels at that substation.
ContributorsShaikh, Mohammed Mubashir (Author) / Qin, Jiangchao (Thesis advisor) / Heydt, Gerald T (Committee member) / Lei, Qin (Committee member) / Arizona State University (Publisher)
Created2018
Description
Brain Computer Interfaces are becoming the next generation controllers not only in the medical devices for disabled individuals but also in the gaming and entertainment industries. In order to build an effective Brain Computer Interface, which accurately translates the user thoughts into machine commands, it is important to have robust and fail proof signal processing and machine learning modules which operate on the raw EEG signals and estimate the current thought of the user.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
In this thesis, several techniques used to perform EEG signal pre-processing, feature extraction and signal classification have been discussed, implemented, validated and verified; efficient supervised machine learning models, for the EEG motor imagery signal classification are identified. To further improve the performance of system unsupervised feature learning techniques have been investigated by pre-training the Deep Learning models. Use of pre-training stacked autoencoders have been proposed to solve the problems caused by random initialization of weights in neural networks.
Motor Imagery (imaginary hand and leg movements) signals are acquire using the Emotiv EEG headset. Different kinds of features like mean signal, band powers, RMS of the signal have been extracted and supplied to the machine learning (ML) stage, wherein, several ML techniques like LDA, KNN, SVM, Logistic regression and Neural Networks are applied and validated. During the validation phase the performances of various techniques are compared and some important observations are reported. Further, deep Learning techniques like autoencoding have been used to perform unsupervised feature learning. The reliability of the features is analyzed by performing classification by using the ML techniques mentioned earlier. The performance of the neural networks has been further improved by pre-training the network in an unsupervised fashion using stacked autoencoders and supplying the stacked autoencoders’ network parameters as initial parameters to the neural network. All the findings in this research, during each phase (pre-processing, feature extraction, classification) are directly relevant and can be used by the BCI research community for building motor imagery based BCI applications.
Additionally, this thesis attempts to develop, test, and compare the performance of an alternative method for classifying human driving behavior. This thesis proposes the use of driver affective states to know the driving behavior. The purpose of this part of the thesis was to classify the EEG data collected from several subjects while driving simulated vehicle and compare the classification results with those obtained by classifying the driving behavior using vehicle parameters collected simultaneously from all the subjects. The objective here is to see if the drivers’ mental state is reflected in his driving behavior.
ContributorsManchala, Vamsi Krishna (Author) / Redkar, Sangram (Thesis advisor) / Rogers, Bradley (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2015
Description
Shunt capacitors are often added in transmission networks at suitable locations to improve the voltage profile. In this thesis, the transmission system in Arizona is considered as a test bed. Many shunt capacitors already exist in the Arizona transmission system and more are planned to be added. Addition of these shunt capacitors may create resonance conditions in response to harmonic voltages and currents. Such resonance, if it occurs, may create problematic issues in the system. It is main objective of this thesis to identify potential problematic effects that could occur after placing new shunt capacitors at selected buses in the Arizona network. Part of the objective is to create a systematic plan for avoidance of resonance issues.
For this study, a method of capacitance scan is proposed. The bus admittance matrix is used as a model of the networked transmission system. The calculations on the admittance matrix were done using Matlab. The test bed is the actual transmission system in Arizona; however, for proprietary reasons, bus names are masked in the thesis copy in-tended for the public domain. The admittance matrix was obtained from data using the PowerWorld Simulator after equivalencing the 2016 summer peak load (planning case). The full Western Electricity Coordinating Council (WECC) system data were used. The equivalencing procedure retains only the Arizona portion of the WECC.
The capacitor scan results for single capacitor placement and multiple capacitor placement cases are presented. Problematic cases are identified in the form of ‘forbidden response. The harmonic voltage impact of known sources of harmonics, mainly large scale HVDC sources, is also presented.
Specific key results for the study indicated include:
• The forbidden zones obtained as per the IEEE 519 standard indicates the bus 10 to be the most problematic bus.
• The forbidden zones also indicate that switching values for the switched shunt capacitor (if used) at bus 3 should be should be considered carefully to avoid resonance condition from existing.
• The highest sensitivity of 0.0033 per unit for HVDC sources of harmonics was observed at bus 7 when all the HVDC sources were active at the same time.
For this study, a method of capacitance scan is proposed. The bus admittance matrix is used as a model of the networked transmission system. The calculations on the admittance matrix were done using Matlab. The test bed is the actual transmission system in Arizona; however, for proprietary reasons, bus names are masked in the thesis copy in-tended for the public domain. The admittance matrix was obtained from data using the PowerWorld Simulator after equivalencing the 2016 summer peak load (planning case). The full Western Electricity Coordinating Council (WECC) system data were used. The equivalencing procedure retains only the Arizona portion of the WECC.
The capacitor scan results for single capacitor placement and multiple capacitor placement cases are presented. Problematic cases are identified in the form of ‘forbidden response. The harmonic voltage impact of known sources of harmonics, mainly large scale HVDC sources, is also presented.
Specific key results for the study indicated include:
• The forbidden zones obtained as per the IEEE 519 standard indicates the bus 10 to be the most problematic bus.
• The forbidden zones also indicate that switching values for the switched shunt capacitor (if used) at bus 3 should be should be considered carefully to avoid resonance condition from existing.
• The highest sensitivity of 0.0033 per unit for HVDC sources of harmonics was observed at bus 7 when all the HVDC sources were active at the same time.
ContributorsPatil, Hardik U (Author) / Heydt, Gerald T (Thesis advisor) / Karady, George G. (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2015