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Analysis of Pre-ictal and Non-Ictal EEG Activity: An EMOTIV and LabVIEW Approach: In the past few years, the study of electrical activity in the brain and its interactions with the body has become popular among researchers. One of the hottest topics related to brain activity is the epileptic seizure prediction. Currently, there are several techniques on how to predict a seizure; however, most of the techniques found in research papers are just mathematical models and not system implementations. The seizure prediction approach proposed in this thesis paper is achieved using the EMOTIV Epoc+ headset, MATLAB, and LabVIEW as the analog and digital signal processing devices. In addition, this thesis project incorporates the use of the Hilbert Huang transform (HHT) method to obtain intrinsic mode functions (IMF) and instantaneous frequency components of the transform. From the IMFs, features as variation coefficient (VC) and fluctuation indexes (FI) are extracted to feed a support vector machine that classifies the EEG data as pre-ictal and non-ictal EEGs. Outstanding patterns in non-ictal and pre-ictal are observed and demonstrated by significant differences between both types of EEG signals. In other words, a classification of EEG signals according to a category can be achieved proving that an epileptic seizure prediction technology has a future in engineering and biotechnology fields.
Analysis of the Integration of LEO Satellite Constellations into 5G Networks: Low Earth orbit (LEO) satellite systems have been proposed as a resource for combating the challenges in 5G network coverage and expanding connectivity to a global realm. This research focuses on the current architecture of LEO satellite constellations, with an emphasis on satellite coverage, visibility patterns and coordination schemes. Key-elements of integrating LEO satellites into the eMBB component of 5G are presented and a breakdown of potential link channel characteristics and physical layer performance metrics are described. The produced information allows for a justified analysis on the conceptualized integration.
An Application of Digital Video Recording and Off-grid Technology to Burrowing Owl Conservation Research: Through this research, engineering students and conservation biologists constructed an off-grid video system for observing western burrowing owls in El Paso, Texas. The burrowing owl has a declining population and their range decreasing, driving scientists' interest to see inside the den for observing critical nesting behavior. Texas Parks and Wildlife Department (TPWD) biologists wanted videos from inside the dark, isolated hillside owl burrows. This research yielded a replicable multi-camera prototype, empowering others to explore applications of engineering and wildlife monitoring. The remote station used an off-the-shelf video recording system, solar panels, charge controller, and lead acid batteries. Four local K-12 science educators participated in system testing at Lake Ray Roberts State Park through the Research Experiences for Teachers (RET, NSF #1132585) program, as well as four undergraduate engineering students as senior design research.
Applications of Machine Learning for Remote Sensing and Environmental Monitoring: This thesis covers applications of machine learning to the fields of remote sensing and environmental monitoring. First, a generalized background on the concepts, tools, and methods used throughout the remainder of the research project are introduced. Chapter 3 covers the implementation of artificial neural networks to improve low-cost particulate matter sensing networks using collocated high-quality sensors with varying dataset parameters. In Chapter 4, an attention-enhanced LSTM-Convolutional neural network is presented to reconstruct satellite-based aerosol optical depth data lost to atmospheric interference. Chapter 5 applies attention mechanisms and convolutional neural networks to the reconstruction and upsampling of satellite-based land surface temperature maps. Chapter 6 presents a model employing geospatial techniques and machine learning methods with a combination of ground-based and remote sensing data to produce a daily ultra-high resolution 30 meter mapping of the PM2.5 concentration across Denton County, Texas.
Applied Real-Time Integrated Distributed Control Systems: An Industrial Overview and an Implemented Laboratory Case Study: This thesis dissertation mainly compares and investigates laboratory study of different implementation methodologies of applied control systems and how they can be adopted in industrial, as well as commercial, automation applications. Namely the research paper aims to assess or evaluate eventual feedback control loops' performance and robustness over multiple conventional or state-of-the-art technologies in the field of applied industrial automation and instrumentation by implementing a laboratory case study setup: the ball on beam system. Hence, the paper tries to close the gap between industry and academia by: first, conducting a historical study and background information of main evolutional and technological eras in the field of industrial process control automation and instrumentation. Then, some related basic theoretical as well as practical concepts are reviewed in Chapter 2 of the report before displaying the detailed design. After that, the next Chapter, analyses the ball on beam control system problem as the case studied in the context of this research through reviewing previous literature, modeling and simulation. The following Chapter details the proposed design and implementation of the ball on beam case study as if it is under the introduced distributed industrial automation architecture. Finally, Chapter 5 concludes this work by listing several points leaned, remarks, and observations, and stating possible development and the future vision of this research.
An Arduino Based Control System for a Brackish Water Desalination Plant: Water scarcity for agriculture is one of the most important challenges to improve food security worldwide. In this thesis we study the potential to develop a low-cost controller for a small scale brackish desalination plant that consists of proven water treatment technologies, reverse osmosis, cation exchange, and nanofiltration to treat groundwater into two final products: drinking water and irrigation water. The plant is powered by a combination of wind and solar power systems. The low-cost controller uses Arduino Mega, and Arduino DUE, which consist of ATmega2560 and Atmel SAM3X8E ARM Cortex-M3 CPU microcontrollers. These are widely used systems characterized for good performance and low cost. However, Arduino also requires drivers and interfaces to allow the control and monitoring of sensors and actuators. The thesis explains the process, as well as the hardware and software implemented.
Arduino Based Hybrid MPPT Controller for Wind and Solar: Renewable power systems are becoming more affordable and provide better options than fossil-fuel generation, for not only the environment, but a benefit of a reduced cost of operation. Methods to optimize charging batteries from renewable technologies is an important subject for off-grid and micro-grids, and is becoming more relevant for larger installations. Overcharging or undercharging the battery can result in failure and reduction of battery life. The Arduino hybrid MPPT controller takes the advantage of solar and wind energy sources by controlling two systems simultaneously. The ability to manage two systems with one controller is better for an overall production of energy, cost, and manageability, at a minor expense of efficiency. The hybrid MPPT uses two synchronous buck DC-DC converters to control both wind and solar. The hybrid MPPT performed at a maximum of 93.6% efficiency, while the individual controller operated at a maximum 97.1% efficiency when working on the bench test. When designing the controller to manage power production from a larger generator, the inductor size was too large due to the frequency provided by the Arduino. A larger inductor means less allowable current to flow before the inductor becomes over saturated, reducing the efficiency of the controller. Utilizing a different microcontroller like the PIC16C63A produces a much faster frequency, which will reduce the inductor size needed and allow more current before over saturation.
The Art and Science of Data Analysis: This thesis aims to utilize data analysis and predictive modeling techniques and apply them in different domains for gaining insights. The topics were chosen keeping the same in mind. Analysis of customer interests is a crucial factor in present marketing trends and hence we worked on twitter data which is a significant part of digital marketing. Neuroscience, especially psychological behavior, is an important research area. We chose eye tracking data based on which we differentiated human concentration while watching controllable (video game) videos and uncontrollable (sports) videos. Currently, cities are using data analysis for becoming smart cities. We worked on the City of Lewisville emergency services data and predicted the vehicle-accident-prone areas for development of precautionary measures in those areas.
Assistive Navigation Technology for Visually Impaired Individuals: Sight is essential in our daily tasks. Compensatory senses have been used for centuries by visually impaired individuals to navigate independently. The help of technology can minimize some challenges for visually impaired individuals. Assistive navigation technologies facilitate the pathfinding and tracing in indoor scenarios. Different modules are added to assistive navigation technologies to warn about the obstacles not only on the ground but about hanging objects. In this work, we attempt to explore new methods to assist visually impaired individuals in navigating independently in an indoor scenario. We employed a location estimation algorithm based on the fingerprinting method to estimate the initial location of the user. We mitigate the error of estimation with particle filter. The shortest path has been calculated with an A* algorithm. To provide the user with an accident-free experiment, we employed an obstacle avoidance algorithm capable of warning the users about the potential hazards. Finally, to provide an effective means of communication with the user, we employed text-to-speech and speech recognition algorithms. The main contribution of this work is to glue these modules together efficiently and affordably.
Asynchronous Level Crossing ADC for Biomedical Recording Applications: This thesis focuses on the recording challenges faced in biomedical systems. More specifically, the challenges in neural signal recording are explored. Instead of the typical synchronous ADC system, a level crossing ADC is detailed as it has gained recent interest for low-power biomedical systems. These systems take advantage of the time-sparse nature of the signals found in this application. A 10-bit design is presented to help capture the lower amplitude action potentials (APs) in neural signals. The design also achieves a full-scale bandwidth of 1.2 kHz, an ENOB of 9.81, a power consumption of 13.5 microwatts, operating at a supply voltage of 1.8 V. This design was simulated in Cadence using 180 nm CMOS technology.
Automatic Extraction of Highlights from a Baseball Video Using HMM and MPEG-7 Descriptors: In today’s fast paced world, as the number of stations of television programming offered is increasing rapidly, time accessible to watch them remains same or decreasing. Sports videos are typically lengthy and they appeal to a massive crowd. Though sports video is lengthy, most of the viewer’s desire to watch specific segments of the video which are fascinating, like a home-run in a baseball or goal in soccer i.e., users prefer to watch highlights to save time. When associated to the entire span of the video, these segments form only a minor share. Hence these videos need to be summarized for effective presentation and data management. This thesis explores the ability to extract highlights automatically using MPEG-7 features and hidden Markov model (HMM), so that viewing time can be reduced. Video is first segmented into scene shots, in which the detection of the shot is the fundamental task. After the video is segmented into shots, extraction of key frames allows a suitable representation of the whole shot. Feature extraction is crucial processing step in the classification, video indexing and retrieval system. Frame features such as color, motion, texture, edges are extracted from the key frames. A baseball highlight contains certain types of scene shots and these shots follow a particular transition pattern. The shots are classified as close-up, out-field, base and audience. I first try to identify the type of the shot using low level features extracted from the key frames of each shot. For the identification of the highlight I use the hidden Markov model using the transition pattern of the shots in time domain. Experimental results suggest that with reasonable accuracy highlights can be extracted from the video.
Baseband Noise Suppression in Ofdm Using Kalman Filter: As the technology is advances the reduced size of hardware gives rise to an additive 1/f baseband noise. This additive 1/f noise is a system noise generated due to miniaturization of hardware and affects the lower frequencies. Though 1/f noise does not show much effect in wide band channels because of its nature to affect only certain frequencies, 1/f noise becomes a prominent in OFDM communication systems where narrow band channels are used. in this thesis, I study the effects of 1/f noise on the OFDM systems and implement algorithms for estimation and suppression of the noise using Kalman filter. Suppression of the noise is achieved by subtracting the estimated noise from the received noise. I show that the performance of the system is considerably improved by applying the 1/f noise suppression.
A Bidirectional Two-Hop Relay Network Using GNU Radio and USRP: A bidirectional two-hop relay network with decode-and-forward strategy is implemented using GNU Radio (software) and several USRPs (hardware) on Ubuntu (operating system). The relay communication system is comprised of three nodes; Base Station A, Base Station B, and Relay Station (the intermediate node). During the first time slot, Base Station A and Base Station B will each transmit data, e.g., a JPEG file, to Relay Station using DBPSK modulation and FDMA. For the final time slot, Relay Station will perform a bitwise XOR of the data, and transmit the XORed data to Base Station A and Base Station B, where the received data is decoded by performing another XOR operation with the original data.
BLE Controller Module for Wireless Sensor Networks: Sensors have been an integral part of our life since a long time. Traditionally, the transmit information to a data collection center through a physical wire. However, with the introduction of Bluetooth Low Energy (BLE) communication protocol, more research is being done into the field of wireless sensor networks (WSN). BLE was introduced to target low power applications. The CC2650 Launchpad designed by Texas Instruments (TI) can lead to a bulky final product. The aim was to design hardware for the CC2650 micro-controller with the aim of making it more compact for use in WSNs. A top-down approach was used wherein the available product is studied to identify the redundant and reverse engineer it to design a new product. A 2 layer printed circuit board (PCB) was designed which resulted in a 64 percent decrease in size compared to the Launchpad. Also, experiments were performed to test the proof of concept.
Case Studies to Learn Human Mapping Strategies in a Variety of Coarse-Grained Reconfigurable Architectures: Computer hardware and algorithm design have seen significant progress over the years. It is also seen that there are several domains in which humans are more efficient than computers. For example in image recognition, image tagging, natural language understanding and processing, humans often find complicated algorithms quite easy to grasp. This thesis presents the different case studies to learn human mapping strategy to solve the mapping problem in the area of coarse-grained reconfigurable architectures (CGRAs). To achieve optimum level performance and consume less energy in CGRAs, place and route problem has always been a major concern. Making use of human characteristics can be helpful in problems as such, through pattern recognition and experience. Therefore to conduct the case studies a computer mapping game called UNTANGLED was analyzed as a medium to convey insights of human mapping strategies in a variety of architectures. The purpose of this research was to learn from humans so that we can come up with better algorithms to outperform the existing algorithms. We observed how human strategies vary as we present them with different architectures, different architectures with constraints, different visualization as well as how the quality of solution changes with experience. In this work all the case studies obtained from exploiting human strategies provide useful feedback that can improve upon existing algorithms. These insights can be adapted to find the best architectural solution for a particular domain and for future research directions for mapping onto mesh-and- stripe based CGRAs.
Characterization of Ecg Signal Using Programmable System on Chip: Electrocardiography (ECG) monitor is a medical device for recording the electrical activities of the heart using electrodes placed on the body. There are many ECG monitors in the market but it is essential to find the accuracy with which they generate results. Accuracy depends on the processing of the ECG signal which contains several noises and the algorithms used for detecting peaks. Based on these peaks the abnormality in the functioning of the heart can be estimated. Hence this thesis characterizes the ECG signal which helps to detect the abnormalities and determine the accuracy of the system.
The Chief Security Officer Problem: The Chief Security Officer Problem (CSO) consists of a CSO, a group of agents trying to communicate with the CSO and a group of eavesdroppers trying to listen to the conversations between the CSO and its agents. Through Lemmas and Theorems, several Information Theoretic questions are answered.
A Cognitive MIMO OFDM Detector Design for Computationally Efficient Space-Time Decoding: In this dissertation a computationally efficient cognitive multiple-input multiple-output (MIMO) orthogonal frequency division duplexing (OFDM) detector is designed to decode perfect space-time coded signals which are able maximize the diversity and multiplexing properties of a rich fading MIMO channel. The adaptive nature of the cognitive detector allows a MIMO OFDM communication system to better meet to needs of future wireless communication networks which require both high reliability and low run-time complexity depending on the propagation environment. The cognitive detector in conjunction with perfect space-time coding is able to achieve up to a 2 dB bit-error rate (BER) improvement at low signal-to-noise ratio (SNR) while also achieving comparable runtime complexity in high SNR scenarios.
A Cognitive Radio Application through Opportunistic Spectrum Access: In wireless communication systems, one of the most important resources being focused on all the researchers is spectrum. A cognitive radio (CR) system is one of the efficient ways to access the radio spectrum opportunistically, and efficiently use the available underutilized licensed spectrum. Spectrum utilization can be significantly enhanced by developing more applications with adopting CR technology. CR systems are implemented using a radio technology called software-defined radios (SDR). SDR provides a flexible and cost-effective solution to fulfil the requirements of end users. We can see a lot of innovations in Internet of Things (IoT) and increasing number of smart devices. Hence, a CR system application involving an IoT device is studied in this thesis. Opportunistic spectrum access involves two tasks of CR system: spectrum sensing and dynamic spectrum access. The functioning of the CR system is rest upon the spectrum sensing. There are different spectrum sensing techniques used to detect the spectrum holes and a few of them are discussed here in this thesis. The simplest and easiest to implement energy detection spectrum sensing technique is used here to implement the CR system. Dynamic spectrum access involves different models and strategies to access the spectrum. Amongst the available models, an interweave model is more challenging and is used in this thesis. Interweave model needs effective spectrum sensing before accessing the spectrum opportunistically. The system designed and simulated in this thesis is capable of transmitting an output from an IoT device using USRP and GNU radio through accessing the radio spectrum opportunistically.
Communication System over Gnu Radio and OSSIE: GNU Radio and OSSIE (Open-Source SCA (Software communication architecture) Implementation-Embedded) are two open source software toolkits for SDR (Software Defined Radio) developments, both of them can be supported by USRP (Universal Software Radio Peripheral). In order to compare the performance of these two toolkits, an FM receiver over GNU Radio and OSSIE are tested in my thesis, test results are showed in Chapter 4 and Chapter 5. Results showed that the FM receiver over GNU Radio has better performance, due to the OSSIE is lack of synchronization between USRP interface and the modulation /demodulation components. Based on this, the SISO (Single Input Single Output) communication system over GNU Radio is designed to transmit and receive sound or image files between two USRP equipped with RFX2400 transceiver at 2.45G frequency. Now, GNU Radio and OSSIE are widely used for academic research, but the future work based on GNU Radio and OSSIE can be designed to support MIMO, sensor network, and real time users etc.
Comparison of Source Diversity and Channel Diversity Methods on Symmetric and Fading Channels.: Channel diversity techniques are effective ways to combat channel fading and noise in communication systems. In this thesis, I compare the performance of source and channel diversity techniques on fading and symmetric continuous channels. My experiments suggest that when SNR is low, channel diversity performs better, and when SNR is high, source diversity shows better performance than channel diversity.
A Comprehensive Modeling Framework for Airborne Mobility: Mobility models serve as the foundation for evaluating and designing airborne networks. Due to the significant impact of mobility models on the network performance, mobility models for airborne networks (ANs) must realistically capture the attributes of ANs. In this paper, I develop a comprehensive modeling framework for ANs. The work I have done is concluded as the following three parts. First, I perform a comprehensive and comparative analysis of AN mobility models and evaluate the models based on several metrics: 1) networking performance, 2) ability to capture the mobility attributes of ANs, 3) randomness levels and 4) associated applications. Second, I develop two 3D mobility models and realistic boundary models. The mobility models follow physical laws behind aircraft maneuvering and therefore capture the characteristics of aircraft trajectories. Third, I suggest an estimation procedure to extract parameters in one of the models that I developed from real flight test data. The good match between the estimated trajectories and real flight trajectories also validate the suitability of the model. The mobility models and the estimation procedure lead to the creation of “realistic” simulation and evaluation environment for airborne networks.
Conditional Disclosure of Secrets and Storage over Graphs: In the era of big data, it is essential to implement practical security and privacy measures to ensure the lawful use of data and provide users with trust and assurance. In the dissertation, I address this issue through several key steps. Firstly, I delve into the problem of conditional secret disclosure, representing it using graphs to determine the most efficient approach for storing and disclosing secrets. Secondly, I extend the conditional disclosure of secrets problem from a single secret to multiple secrets and from a bipartite graph to an arbitrary graph. Thirdly, I remove security constraints to observe how they affect the efficiency of storage and recovery. In our final paper, I explore the secure summation problem, aiming to determine the capacity of total noise. Throughout the dissertation, I leverage information-theoretic tools to address security and privacy concerns.
Consensus Building in Sensor Networks and Long Term Planning for the National Airspace System: In this thesis, I present my study on the impact of multi-group network structure on the performance of consensus building strategies, and the preliminary mathematical formulation of the problem on improving the performance of the National Airspace system (NAS) through long-term investment. The first part of the thesis is concerned with a structural approach to the consensus building problem in multi-group distributed sensor networks (DSNs) that can be represented by bipartite graph. Direct inference of the convergence behavior of consensus strategies from multi-group DSN structure is one of the contributions of this thesis. The insights gained from the analysis facilitate the design and development of DSNs that meet specific performance criteria. The other part of the thesis is concerned with long-term planning and development of the NAS at a network level, by formulating the planning problem as a resource allocation problem for a flow network. The network-level model viewpoint on NAS planning and development will give insight to the structure of future NAS and will allow evaluation of various paradigms for the planning problem.
A Convergence Analysis of LDPC Decoding Based on Eigenvalues: Low-density parity check (LDPC) codes are very popular among error correction codes because of their high-performance capacity. Numerous investigations have been carried out to analyze the performance and simplify the implementation of LDPC codes. Relatively slow convergence of iterative decoding algorithm affects the performance of LDPC codes. Faster convergence can be achieved by reducing the number of iterations during the decoding process. In this thesis, a new approach for faster convergence is suggested by choosing a systematic parity check matrix that yields lowest Second Smallest Eigenvalue Modulus (SSEM) of its corresponding Laplacian matrix. MATLAB simulations are used to study the impact of eigenvalues on the number of iterations of the LDPC decoder. It is found that for a given (n, k) LDPC code, a parity check matrix with lowest SSEM converges quickly as compared to the parity check matrix with high SSEM. In other words, a densely connected graph that represents the parity check matrix takes more iterations to converge than a sparsely connected graph.
The Convolutional Recurrent Structure in Computer Vision Applications: By organically fusing the methods of convolutional neural network (CNN) and recurrent neural network (RNN), this dissertation focuses on the application of optical character recognition and image classification processing. The first part of this dissertation presents an end-to-end novel receipt recognition system for capturing effective information from receipts (CEIR). The main contributions of this research part are divided into three parts. First, this research develops a preprocessing method for receipt images. Second, the modified connectionist text proposal network is introduced to execute text detection. Third, the CEIR combines the convolutional recurrent neural network with the connectionist temporal classification with maximum entropy regularization as a loss function to update the weights in networks and extract the characters from receipt. The CEIR system is validated with the scanned receipts optical character recognition and information extraction (SROIE) database. Furthermore, the CEIR system has strong robustness and can be extended to a variety of different scenarios beyond receipts. For the convolutional recurrent structure application of land use image classification, this dissertation comes up with a novel deep learning model for land use classification, the convolutional recurrent land use classifier (CRLUC), which further improves the accuracy in classifying remote sensing land use images. Besides, the convolutional fully-connected neural networks with hard sample memory pool structure (CFMP) is invented to tackle the remote sensing land use image classification tasks. The CRLUC and CFMP algorithm performances are tested in popular datasets. Experimental studies show the proposed algorithms can classify images with higher accuracy and fewer training episodes compared to popular image classification algorithms.
Data Compression Using a Multi-residue System (Mrs): This work presents a novel technique for data compression based on multi-residue number systems. The basic theorem is that an under-determined system of congruences could be solved to accomplish data compression for a signal satisfying continuity of its information content and bounded in peak-to -peak amplitude by the product of relatively prime moduli,. This thesis investigates this property and presents quantitative results along with MATLAB codes. Chapter 1 is introductory in nature and Chapter 2 deals in more detail with the basic theorem. Chapter 3 explicitly mentions the assumptions made and chapter 4 shows alternative solutions to the Chinese remainder theorem. Chapter 5 explains the experiments in detail whose results are mentioned in chapter 6. Chapter 7 concludes with a summary and suggestions for future work.
Data Transmission in Quantized Consensus: In the world of networked system, average consensus is an important dimension of co-ordinate control and cooperation. Since the communication medium is digital, real value cannot be transmitted and we need to perform quantization before data transmission. But for the quantization, error is introduced in exact value and initial average is lost. Based on this limitation, my 16 bit quantization method (sending MSB in 1-4 cycle and MSB+LSB in 5th cycle) reduces error significantly and preserves initial average. Besides, it works on all types of graphs (star, complete, ring, random geometric graph). My other algorithm, distributing averaging algorithm (PQDA) with probabilistic quantization also works on random geometric graph, star, ring and slow co-herency graph. It shows significant reduced error and attain strict consensus.
Deep Learning Approach for Sensing Cognitive Radio Channel Status: Cognitive Radio (CR) technology creates the opportunity for unlicensed users to make use of the spectral band provided it does not interfere with any licensed user. It is a prominent tool with spectrum sensing functionality to identify idle channels and let the unlicensed users avail them. Thus, the CR technology provides the consumers access to a very large spectrum, quality spectral utilization, and energy efficiency due to spectral load balancing. However, the full potential of the CR technology can be realized only with CRs equipped with accurate mechanisms to predict/sense the spectral holes and vacant spectral bands without any prior knowledge about the characteristics of traffic in a real-time environment. Multi-layered perception (MLP), the popular neural network trained with the back-propagation (BP) learning algorithm, is a keen tool for classification of the spectral bands into "busy" or "idle" states without any a priori knowledge about the user system features. In this dissertation, we proposed the use of an evolutionary algorithm, Bacterial Foraging Optimization Algorithm (BFOA), for the training of the MLP NN. We have compared the performance of the proposed system with the traditional algorithm and with the Hybrid GA-PSO method. With the results of a simulation experiment that this new learning algorithm for prediction of channel states outperforms the traditional BP algorithm and Hybrid GA-PSO method with respect to classification accuracy, probability of misdetection, and Probability of false alarm.
Design and Application of a New Planar Balun: The baluns are the key components in balanced circuits such balanced mixers, frequency multipliers, push–pull amplifiers, and antennas. Most of these applications have become more integrated which demands the baluns to be in compact size and low cost. In this thesis, a new approach about the design of planar balun is presented where the 4-port symmetrical network with one port terminated by open circuit is first analyzed by using even- and odd-mode excitations. With full design equations, the proposed balun presents perfect balanced output and good input matching and the measurement results make a good agreement with the simulations. Second, Yagi-Uda antenna is also introduced as an entry to fully understand the quasi-Yagi antenna. Both of the antennas have the same design requirements and present the radiation properties. The arrangement of the antenna’s elements and the end-fire radiation property of the antenna have been presented. Finally, the quasi-Yagi antenna is used as an application of the balun where the proposed balun is employed to feed a quasi-Yagi antenna. The antenna is working in the S-band radio frequency and achieves a measured 36% fractional bandwidth for return loss less than -10 dB. The antenna demonstrates a good agreement between its measurement and simulation results. The impact of the parasitic director on the antenna’s performance is also investigated. The gain and the frequency range of the antenna have been reduced due to the absence of this element. This reduction presents in simulation and measurement results with very close agreement.
Design and Application of Phased Array System: Since its invention, phased array has been extensively applied in both military and civil areas. The applications include target detecting and tracking, space probe communication, broadcasting, human-machine interfaces, and remote sensing. Although the phased array applications show a broad range of potential market, there are some limitations of phased array's development: high cost, complex structure, narrow bandwidth, and high power consumption. Therefore, novel ideas are needed to reduce these constraints. In this thesis, several new approaches about the design and application of phased array are presents. First, the principle of phased array and fundamental design equations are introduced. Second, a new application of phased array antenna for radar respiration measurement is presented. By integrating a 4×4 Butler matrix with four-element antenna array, there will be four distinct main beams in radiation pattern. This new approach can improve the measurement accuracy and realize a high detecting rate. Third, a compact phased array antenna system based on dual-band operations is introduced. Dual-band function can make N-antenna system obtain 2N unique radiation beams (N is an integer) and achieve a significant size reduction compared to the conventional single-band system. To verify the design concept, a four-element phased array antenna working at 5GHz and 8GHz is designed and fabricated. The measurement results make a good agreement with the simulations. Finally, a novel architecture of steering phase feeding network by using bi-directional series-fed topology is presented. This bi-directional series-fed network needs less phase shifters and realizes steering phase function by applying control voltage.
The Design and Implementation of an Effective Vision-Based Leader-Follower Tracking Algorithm Using PI Camera: The thesis implements a vision-based leader-follower tracking algorithm on a ground robot system. One camera is the only sensor installed the leader-follower system and is mounted on the follower. One sphere is the only feature installed on the leader. The camera identifies the sphere in the openCV Library and calculates the relative position between the follower and leader using the area and position of the sphere in the camera frame. A P controller for the follower and a P controller for the camera heading are built. The vision-based leader-follower tracking algorithm is verified according to the simulation and implementation.
Design and Implementation of Broad Band and Narrow Band Antennas and Their Applications: The thesis deals with the design and implementation of broadband and narrowband antennas and their applications in practical environment. In this thesis, a new concept for designing the UWB antenna is proposed based on the CRLH metamaterials and this UWB antenna covers a frequency range from 2.45 GHz to 11.6 GHz. Based on the design of the UWB antenna, another antenna is developed that can cover a very wide bandwidth i.e from 0.66 GHz to 120 GHz. This antenna can not only be used for UWB applications but also for other communication systems working below the UWB spectrum such as GSM, GPS, PCS and Bluetooth. The proposed antenna covering the bandwidth from 0.66 GHz to 120 GHz is by far the largest bandwidth antenna developed based on metamaterials. Wide band antennas are not preferred for sensing purpose as it is difficult to differentiate the received signals. A multiband antenna which can be used as a strain sensor for structural health monitoring is proposed. The idea is to correlate the strain applied along the length or width with the multiple resonant frequencies. This gives the advantage of detecting the strain applied along any direction (either length or width), thus increasing the sensing accuracy. Design and application of a narrow-band antenna as a temperature sensor is also presented. This sensor can be used to detect very high temperature changes (>10000C). This sensor does not require a battery, can be probed wirelessly, simple and can be easily fabricated, can withstand harsh environmental conditions.
Design and Implementation of Communication Platform for Autonomous Decentralized Systems: This thesis deals with the decentralized autonomous system, in which individual nodes acting like peers, communicate and participate in collaborative tasks and decision making processes. An experimental test-bed is created using four Garcia robots. The robots act like peers and interact with each other using user datagram protocol (UDP) messages. Each robot continuously monitors for messages coming from other robots and respond accordingly. Each robot broadcasts its location to all the other robots within its vicinity. Robots do not have built-in global positioning system (GPS). So, an indoor localization method based on signal strength is developed to estimate robot's position. The signal strength that the robot gets from the nearby wireless access points is used to calculate the robot's position. Trilateration and fingerprint are some of the indoor localization methods used for this purpose. The communication functionality of the decentralized system has been tested and verified in the autonomous systems laboratory.
Design fully-integrated dual-band two-stage class-E CMOS PA: In retrospect we can see that from the last century, wireless electronic technology has been in a rapid state of development. With the popularity of wireless communication, the power amplifier demand is rising. In general, magnitude, maximum noise figure, minimum noise figure, efficiency, and output power are important indicators of the amplifier. The IC industry is exploring how to reduce the additional cost and improve the high-frequency performance. Therefore, designing a strong adaptability and high cost performance of the PA has become a priority. As these technologies advance, the power amplifiers need to have better integration, lower cost, and lower power dissipation. Also, some special requirements are being asked in some areas, such as multi-mode and multi-band. In general, people have to use several power amplifiers parallel to frame a multifunction chip. Each of them working at different frequencies of interest has to have separate matching network, design, and area; also, the diversity amplifier prices will increase with the number of amplifiers, and its cost is also changed. In this thesis, because Class E power amplifier has lower power dissipation, 100% ideal efficiency, simple circuit structure, and strong applicability, the Class E is used as power amplifier in main stage. Moreover, in order to decrease input power and increase output power, the class A power amplifier is used as driver stage. It can use very small amount of power to provide a larger power. Moreover, we use a switched variable inductor and capacitor to constitute a dual band matching network which can let the PA work at more than one frequency. In fact, we design a Class A PA which is as a driver stage. Then, when we support 1 dBm input power, the driver stage can have 8 dBm output power. Also the output will be the input power …
Design of a Dual Band Gan Pa Utilizing Dual Band Impedance Transformers: This thesis discusses the design, fabrication, and testing of a high efficiency, dual band radio frequency power amplifier. While it is difficult to demonstrate an exact mode of operation for power amplifiers at radio frequencies, based on the characteristics of the transistor itself, the argument can be made that our high efficiency performance is due to an approximation to class E operation. The PA is designed around a CGH40025 transistor manufactured by Cree, Inc, which has developed a very useful nonlinear model of its transistor, which allows use of software load/source pull methods to determine optimum impedances to be presented to the gate and drain (hereafter referred to as source and load) of the transistor at each band of operation. A recent work on dual-band impedance matching is then used to design distributed element networks in order to present conjugate matches of these impedances to the transistor. This is followed by a careful layout, after which the PA is then fabricated on a low-impedance substrate using a LPKF Protomat S63 rapid prototyping machine. Measurements of gain and drain current provide values for power-added-efficiency. Simulated gains were 21 and 18 dB at 800 MHz and 1.85 GHz, respectively, with PAE around 63% for both bands. Measurements taken from the fabricated PA showed gains of 20 and 16 dB at each band, but PAE of 80% at 800 MHz and 43% at 1.85 GHz.
Design of a Wearable Flexible Resonant Body Temperature Sensor with Inkjet-Printing: A wearable body temperature sensor would allow for early detection of fever or infection, as well as frequent and accurate hassle-free recording. This thesis explores the design of a body-temperature-sensing device inkjet-printed on a flexible substrate. All structures were first modeled by first-principles, theoretical calculations, and then simulated in HFSS. A variety of planar square inductor geometries were studied before selecting an optimal design. The designs were fabricated using multiple techniques and compared to the simulation results. It was determined that inductance must be carefully measured and documented to ensure good functionality. The same is true for parallel-plate and interdigitated capacitors. While inductance remains relatively constant with temperature, the capacitance of the device with a temperature-sensitive dielectric layer will result in a shift in the resonant frequency as environmental or ambient temperature changes. This resonant frequency can be wirelessly detected, with no battery required for the sensing device, from which the temperature can be deduced. From this work, the optimized version of the design comprises of conductive silver in with a temperature-sensitive graphene oxide layer, intended for inkjet-printing on flexible polyimide substrates. Graphene oxide demonstrates a high dielectric permittivity with good sensing capabilities and high accuracy. This work pushes the state-of-the-art in applying these novel materials and techniques to enable flexible body temperature sensors for future biomedical applications.
Design of a Wideband Class J Power Amplifier: A conventional RF power amplifier will convert the low powered radio frequency signals into high powered signals. Along with the expected ability to increase the communication distance, data transfer rates, RF power amplifiers also have many applications which include military radar system, whether forecasting, etc. The main objective of any power amplifier research is to increase the efficiency while maintaining linearity and broadening the frequency of operation. The main motivation for the renewed interest in PA technology comes from the technical challenges and the economics of modern digital communication systems. Modern communications require high linear power amplifiers and in order to reduce the complete system cost, it is necessary to have a single broadband power amplifier, which can amplify multiple carriers. The improvement in the efficiency of the power amplifier increases the battery life and also reduces the cooling requirements for the same output power. In this thesis, I aim to design and build a wideband class J power amplifier suitable for modern communications. For wideband operation of the GaN technology PA, a bandwidth extension design method is studied and implemented. The simulation results are proved to have a good argument with the theoretical calculations.
Design of Frequency Output Pressure Transducer: Piezoelectricity crystal is used in different area in industry, such as downhole oil, gas industry, and ballistics. The piezoelectricity crystals are able to create electric fields due to mechanical deformation called the direct piezoelectric effect, or create mechanical deformation due to the effect of electric field called the indirect piezoelectric effect. In this thesis, piezoelectricity effect is the core part. There are 4 parts in the frequency output pressure transducer: two crystal oscillators, phase-locked loop (PLL), mixer, frequency counter. Crystal oscillator is used to activate the piezoelectricity crystal which is made from quartz. The resonance frequency of the piezoelectricity crystal will be increased with the higher pressure applied. The signal of the resonance frequency will be transmitted to the PLL. The function of the PLL is detect the frequency change in the input signal and makes the output of the PLL has the same frequency and same phase with the input signal. The output of the PLL will be transmitted to a Mixer. The mixer has two inputs and one output. One input signal is from the pressure crystal oscillator and another one is from the reference crystal oscillator. The frequency difference of the two signal will transmitted to the frequency counter from the output of the mixer. Thus, the frequency output pressure transducer with a frequency counter is a portable device which is able to measure the pressure without oscilloscope or computer.
Design of Low-Power Front End Compressive Sensing Circuitry and Energy Harvesting Transducer Modeling for Self-Powered Motion Sensor: Compressed sensing (CS) is an innovative approach of signal processing that facilitates sub-Nyquist processing of bio-signals, such as a neural signal, electrocardiogram (ECG), and electroencephalogram (EEG). This strategy can be used to lower the data rate to realize ultra-low-power performance, As the count of recording channels increases, data volume is increased resulting in impermissible transmitting power. This thesis work presents the implementation of a CMOS-based front-end design with the CS in the standard 180 nm CMOS process. A novel pseudo-random sequence generator is proposed, which consists of two different types of D flip-flops that are used for obtaining a completely random sequence. This thesis work also includes the (reverse electrowetting-on-dielectric) REWOD based energy harvesting model for self-powered bio-sensor which utilizes the electrical energy generated through the process of conversion of mechanical energy to electrical energy. This REWOD based energy harvesting model can be a good alternative to battery usage, particularly for the bio-wearable applications. The comparative analysis of the results generated for voltage, current and capacitance of the rough surface model is compared to that of results of planar surface REWOD.
Design of Multi Band Microwave Devices Using Coupled Line Transmission Lines: Multi band technology helps in getting multiple operating frequencies using a single microwave device. This thesis presents the design of dual and tri band microwave devices using coupled transmission line structures. Chapter 2 presents the design of a novel dual band transmission line structure using coupled lines. In chapter 3, Design of a dual band branch line coupler and a dual band Wilkinson power divider are proposed using the novel dual band transmission line structure presented in the previous chapter. In chapter 4, Design of a tri band transmission line structure by extending the dual band structure is presented. The Conclusion and future work are presented in chapter 5.
Design of Tunable/Reconfigurable and Compact Microwave Devices: With the rapid development of the modern technology, radio frequency and microwave systems are playing more and more important roles. Since the time the first microwave device was invented, they have been leading not only the military but also our daily life to a new era. In order to make the devices have more practical applications, more and more strict requirements have been imposed. For example, good adaptability, reduced cost and shrank size are highly required. In this thesis, three devices are designed based on this requirement. At first, a symmetric four-port microwave varactor based 90-degree directional coupler with tunable coupling ratios and reconfigurable responses is presented. The proposed coupler is designed based on the modified structure of a crossover, where varactors are loaded. Then, a novel reconfigurable 3-dB directional coupler is presented. Varactors and inductors are loaded to the device to realize the reconfigurable performance. By adjusting the voltage applied to the varactors, the proposed coupler can be reconfigured from a branch-line coupler (90-degree coupler) to a rat-race coupler (180 degree coupler) and vice versa. At last, two types (Type-I and Type-II) of microwave baluns with generalized structures are presented. Different from the conventional transmission-line-based baluns where λ/2 transmission lines or λ/4 coupled lines are used, the proposed baluns are constructed by transmission lines with arbitrary electrical lengths.
Design of Ultra Wideband Low Noise Amplifier for Satellite Communications: This thesis offers the design and improvement of a 2 GHz to 20 GHz low noise amplifier (LNA) utilizing pHEMT technology. The pHEMT technology allows the LNA to generate a boosted signal at a lower noise figure (NF) while consuming less power and achieving smooth overall gain. The design achieves an overall gain (S21) of ≥ 10 dB with an NF ≤ 2 dB while consuming ≤ 30 mA of power while using commercial off-the-shelf (COTS) components.
Design of Voltage Boosting Rectifiers for Wireless Power Transfer Systems: This thesis presents a multi-stage rectifier for wireless power transfer in biomedical implant systems. The rectifier is built using Schottky diodes. The design has been simulated in 0.5µm and 130nm CMOS processes. The challenges for a rectifier in a wireless power transfer systems are observed to be the efficiency, output voltage yield, operating frequency range and the minimum input voltage the rectifier can convert. The rectifier outperformed the contemporary works in the mentioned criteria.
Design Space Exploration of Domain Specific CGRAs Using Crowd-sourcing: CGRAs (coarse grained reconfigurable array architectures) try to ﬁll the gap between FPGAs and ASICs. Over three decades, the research towards CGRA design has produced number of architectures. Each of these designs lie at diﬀerent points on a line drawn between FPGAs and ASICs, depending on the tradeoﬀs and design choices made during the design of architectures. Thus, design space exploration (DSE) takes a very important role in the circuit design process. In this work I propose the design space exploration of CGRAs can be done quickly and eﬃciently through crowd-sourcing and a game driven approach based on an interactive mapping game UNTANGLED and a design environment called SmartBricks. Both UNTANGLED and SmartBricks have been developed by our research team at Reconfigurable Computing Lab, UNT. I present the results of design space exploration of domain-specific reconfigurable architectures and compare the results comparing stripe vs mesh style, heterogeneous vs homogeneous. I also compare the results obtained from diﬀerent interconnection topologies in mesh. These results show that this approach oﬀers quick DSE for designers and also provides low power architectures for a suite of benchmarks. All results were obtained using standard cell ASICs with 90 nm process.
Development and Analysis of a Mobile Node Tracking Antenna Control System: A wireless communication system allows two parties to exchange information over long distances. The antenna is the component of a wireless communication system that allows information to be converted into electromagnetic radiation that propagates through the air. A system using an antenna with a highly directional beam pattern allows for high power transmission and reception of data. For a directional antenna to serve its purpose, it must be accurately pointed at the object it is communicating with. To communicate with a mobile node, knowledge of the mobile node's position must be gained so the directional antenna can be regularly pointed toward the moving target. The Global Positioning System (GPS) provides an accurate source of three-dimensional position information for the mobile node. This thesis develops an antenna control station that uses GPS information to track a mobile node and point a directional antenna toward the mobile node. Analysis of the subsystems used and integrated system test results are provided to assess the viability of the antenna control station.
Development and Application of Novel Computer Vision and Machine Learning Techniques: The following thesis proposes solutions to problems in two main areas of focus, computer vision and machine learning. Chapter 2 utilizes traditional computer vision methods implemented in a novel manner to successfully identify overlays contained in broadcast footage. The remaining chapters explore machine learning algorithms and apply them in various manners to big data, multi-channel image data, and ECG data. L1 and L2 principal component analysis (PCA) algorithms are implemented and tested against each other in Python, providing a metric for future implementations. Selected algorithms from this set are then applied in conjunction with other methods to solve three distinct problems. The first problem is that of big data error detection, where PCA is effectively paired with statistical signal processing methods to create a weighted controlled algorithm. Problem 2 is an implementation of image fusion built to detect and remove noise from multispectral satellite imagery, that performs at a high level. The final problem examines ECG medical data classification. PCA is integrated into a neural network solution that achieves a small performance degradation while requiring less then 20% of the full data size.
Development and Integration of a Low-Cost Occupancy Monitoring System: The world is getting busier and more crowded each year. Due to this fact resources such as public transport, available energy, and usable space are becoming congested and require vast amounts of logistical support. As of February 2018, nearly 95% of Americans own a mobile cell phone according to the Pew Research Center. These devices are consistently broadcasting their presents to other devices. By leveraging this data to provide occupational awareness of high traffic areas such as public transit stops, buildings, etc logistic efforts can be streamline to best suit the dynamics of the population. With the rise of The Internet of Things, a scalable low-cost occupancy monitoring system can be deployed to collect this broadcasted data and present it to logistics in real time. Simple IoT devices such as the Raspberry Pi, wireless cards capable of passive monitoring, and the utilization of specialized software can provide this capability. Additionally, this combination of hardware and software can be integrated in a way to be as simple as a typical plug and play set up making system deployment quick and easy. This effort details the development and integration work done to deliver a working product acting as a foundation to build upon. Machine learning algorithms such as k-Nearest-Neighbors were also developed to estimate a mobile device's approximate location inside a building.
Development of a Cost Effective Wireless Sensor System for Indoor Air Quality Monitoring Applications: Poor air quality can greatly affect the public health. Research studies indicate that indoor air can be more polluted than the outdoor air. An indoor air quality monitoring system will help to create an awareness of the quality of air inside which will eventually help in improving it. The objective of this research is to develop a low cost wireless sensor system for indoor air quality monitoring. The major cost reduction of the system is achieved by using low priced sensors. Interface circuits had to be designed to make these sensors more accurate. The system is capable of measuring carbon dioxide, carbon monoxide, ozone, temperature, humidity and volatile organic compounds. The prototype sensor node modules were developed. The sensor nodes were the connected together by Zigbee network. The nodes were developed in such a way that it is compact in size and wireless connection of sensor nodes enable to collect air quality data from multiple locations simultaneously. The collected data was stored in a computer. We employed linear least-square approach for the calibration of each sensor to derive a conversion formula for converting the sensor readings to engineering units. The system was tested with different pollutants and data collected was compared with a professional grade monitoring system for analyzing its performance. The results indicated that the data from our system matched quite well with the professional grade monitoring system.
Development Of A Testbed For Multimedia Environmental Monitoring: Multimedia environmental monitoring involves capturing valuable visual and audio information from the field station. This will permit the environmentalists and researchers to analyze the habitat and vegetation of a region with respect to other environmental specifics like temperature, soil moisture, etc. This thesis deals with the development of a test bed for multimedia monitoring by capturing image information and making it available for the public. A USB camera and a Single board computer are used to capture images at a specified frequency. A web-client is designed to display the image data and establish a secured remote access to reconfigure the field station. The development includes two modes of image acquisition including a basic activity recognition algorithm. Good quality images are captured with the cost for development of the system being less than 2 hundred dollars.

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