Search Results

Spectrum Analysis and Prediction Using Long Short Term Memory Neural Networks and Cognitive Radios
One statement that we can make with absolute certainty in our current time is that wireless communication is now the standard and the de-facto type of communication. Cognitive radios are able to interpret the frequency spectrum and adapt. The aim of this work is to be able to predict whether a frequency channel is going to be busy or free in a specific time located in the future. To do this, the problem is modeled as a time series problem where each usage of a channel is treated as a sequence of busy and free slots in a fixed time frame. For this time series problem, the method being implemented is one of the latest, state-of-the-art, technique in machine learning for time series and sequence prediction: long short-term memory neural networks, or LSTMs.
Human-Machine Interface Using Facial Gesture Recognition
This Master thesis proposes a human-computer interface for individual with limited hand movements that incorporate the use of facial gesture as a means of communication. The system recognizes faces and extracts facial gestures to map them into Morse code that would be translated in English in real time. The system is implemented on a MACBOOK computer using Python software, OpenCV library, and Dlib library. The system is tested by 6 students. Five of the testers were not familiar with Morse code. They performed the experiments in an average of 90 seconds. One of the tester was familiar with Morse code and performed the experiment in 53 seconds. It is concluded that errors occurred due to variations in features of the testers, lighting conditions, and unfamiliarity with the system. Implementing an auto correction and auto prediction system will decrease typing time considerably and make the system more robust.
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.
Statistical Strategies for Efficient Signal Detection and Parameter Estimation in Wireless Sensor Networks
This dissertation investigates data reduction strategies from a signal processing perspective in centralized detection and estimation applications. First, it considers a deterministic source observed by a network of sensors and develops an analytical strategy for ranking sensor transmissions based on the magnitude of their test statistics. The benefit of the proposed strategy is that the decision to transmit or not to transmit observations to the fusion center can be made at the sensor level resulting in significant savings in transmission costs. A sensor network based on target tracking application is simulated to demonstrate the benefits of the proposed strategy over the unconstrained energy approach. Second, it considers the detection of random signals in noisy measurements and evaluates the performance of eigenvalue-based signal detectors. Due to their computational simplicity, robustness and performance, these detectors have recently received a lot of attention. When the observed random signal is correlated, several researchers claim that the performance of eigenvalue-based detectors exceeds that of the classical energy detector. However, such claims fail to consider the fact that when the signal is correlated, the optimal detector is the estimator-correlator and not the energy detector. In this dissertation, through theoretical analyses and Monte Carlo simulations, eigenvalue-based detectors are shown to be suboptimal when compared to the energy detector and the estimator-correlator.
Parameter Estimation Using Consensus Building Strategies with Application to Sensor Networks
Sensor network plays a significant role in determining the performance of network inference tasks. A wireless sensor network with a large number of sensor nodes can be used as an effective tool for gathering data in various situations. One of the major issues in WSN is developing an efficient protocol which has a significant impact on the convergence of the network. Parameter estimation is one of the most important applications of sensor network. In order to model such large and complex networks for estimation, efficient strategies and algorithms which take less time to converge are being developed. To deal with this challenge, an approach of having multilayer network structure to estimate parameter and reach convergence in less time is estimated by comparing it with known gossip distributed algorithm. Approached Multicast multilayer algorithm on a network structure of Gaussian mixture model with two components to estimate parameters were compared and simulated with gossip algorithm. Both the algorithms were compared based on the number of iterations the algorithms took to reach convergence by using Expectation Maximization Algorithm.Finally a series of theoretical and practical results that explicitly showed that Multicast works better than gossip in large and complex networks for estimation in consensus building strategies.
Development of High Gain Ultraviolet Photo Detectors Based on Zinc Oxide Nanowires
Semiconductor nanowires acts as an emerging class of materials with great potential for applications in future electronic devices. Small size, large surface to volume ratio and high carrier mobility of nanowires make them potentially useful for electronic applications with high integration density. In this thesis, the focus was on the growth of high quality ZnO nanowires, fabrication of field effect transistors and UV- photodetectros based on them. Intrinsic nanowire parameters such as carrier concentration, field effect mobility and resistivity were measured by configuring nanowires as field effect transistors. The main contribution of this thesis is the development of a high gain UV photodetector. A single ZnO nanowire functioning as a UV photodetector showed promising results with an extremely high spectral responsivity of 120 kA/W at wavelength of 370 nm. This corresponds to high photoconductive gain of 2150. To the best of our knowledge, this is the highest responsivity and gain reported so far, the previous values being responsivity=40 kA/W and gain=450. The enhanced photoconductive behavior is attributed to the presence of surface states that acts as hole traps which increase the life time of photogenerated electrons raising the photocurrent. This work provides the evidence of such solid states and preliminary results to modify the surface of ZnO nanowire is also produced.
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.
Investigation of the Effect of Functional Units/Connectivity Arrangement on Energy Consumption of Reconfigurable Architectures Using an Interactive Design Framework
Allocation of expensive resources, (such as Multiplier) onto the CGRA has been of interest from quite some time. For these architectural solutions to fulfill the designers' requirements, it is of utmost importance that the design offers high performance, low power consumption, and effective area utilization. The allocation problem is studied using the UntangledII gaming environment, which has been developed at the Reconfigurable Computing Lab at UNT to discover the design of custom domain-specific architectures. This thesis explores several case-studies to investigate the arrangement of functional units and interconnects to achieve a low power, high performance, and flexible heterogeneous designs that can fit for a suite of applications. In the later part, several human mapping strategies of top and bottom players to design a custom domain-specific architecture are presented. Some common trends that were examined while analyzing the mapping strategies of the players are also discussed.
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.
Formation Control of Multi-Agent Systems
Formation control is a classical problem and has been a prime topic of interest among the scientific community in the past few years. Although a vast amount of literature exists in this field, there are still many open questions that require an in-depth understanding and a new perspective. This thesis contributes towards exploring the wide dimensions of formation control and implementing a formation control scheme for a group of multi-agent systems. These systems are autonomous in nature and are represented by double integrated dynamics. It is assumed that the agents are connected in an undirected graph and use a leader-follower architecture to reach formation when the leading agent is given a velocity that is piecewise constant. A MATLAB code is written for the implementation of formation and the consensus-based control laws are verified. Understanding the effects on formation due to a fixed formation geometry is also observed and reported. Also, a link that describes the functional similarity between desired formation geometry and the Laplacian matrix has been observed. The use of Laplacian matrix in stability analysis of the formation is of special interest.
Optimization of an SDR Based Aerial Base Station
Most times people are unprepared to face natural disasters resulting in chaos, increased number of deaths, etc.Emergency responders need an efficiently working communication network to get in touch with the emergency services like hospitals, police, fire and rescue as well as people who are stranded. Such a network is also the need of the hour for survivors to contact their near and dear ones. One of the major barriers of communication during an emergency is the destruction of network elements. In case the communication devices survive the calamity, odds of the network getting congested are certainly high because almost everyone will be trying to use the same network resources. An important factor when dealing with emergency situations is the calls for an immediate response and an efficient Emergency Communication Systems (ECS). Currently there is a capability gap between existing ECS solutions and what we dream of achieving. Most current solutions do not meet cost or mobility constraints. An inexpensive, portable and mobile system will fulfill this capability gap. The main purpose of this research is to optimize the altitude and received signal strength of an aerial base station to provide maximum radio coverage on the ground as well as propose the best fit radio propagation channel model to carry out the experiment for the current scenario.
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.
Advances to Convolutional Neural Network Architectures for Prediction and Classification with Applications in the First Dimensional Space
In the vast field of signal processing, machine learning is rapidly expanding its domain into all realms. As a constituent of this expansion, this thesis presents contributive work on advancements in machine learning algorithms by building on the shoulder of giants. The first chapter of this thesis contains enhancements to a CNN (convolutional neural network) for better classification of heartbeat arrhythmia. The network goes through a two stage development, the first being augmentations to the network and the second being the implementation of dropout. Chapter 2 involves the combination of CNN and LSTM (long short term memory) networks for the task of short-term energy use data regression. Exploiting the benefits of two of the most powerful neural networks, a unique, novel neural network is created to effectually predict future energy use. The final section concludes this work with directions for future works.
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.
PM2.5 Particle Sensing and Fit Factor Test of a Respirator with SAW-Based Sensor
PM2.5 particle sensing has been done using surface acoustic wave based sensor for two different frequencies. Due to mass loading and elasticity loading on the sensor's surface, the center frequency of the sensor shifts. The particle concentration can be tracked based on that frequency shift. The fit factor test has been conducted using higher frequency SAW sensor. The consist results has been achieved for particle sensing and fit factor test with SAW based sensor.
Electrical Equivalent Modeling of the Reverse Electrowetting-on-Dielectric (REWOD) Based Transducer along with Highly Efficient Energy Harvesting Circuit Design towards Self-Powered Motion Sensor
Among various energy harvesting technologies reverse electrowetting-on-dielectric energy harvesting (REWOD) has been proved to harvest energy from low frequency motion such as many human motion activities (e.g. walking, running, jogging etc.). Voltage rectification and DC-DC boosting of low magnitude AC voltage from REWOD can be used to reliably self-power the wearable sensors. In this work, a commercial component-based rectifier and DC-DC converter is designed and experimentally verified, for further miniaturization standard 180 nm CMOS process is used to design the rectifier and the DC-DC boost converter.This work also includes the MATLAB based model for REWOD energy harvester for various REWOD models. In REWOD energy harvesting, a mechanical input during the motion causes the electrolyte placed in between two dissimilar electrodes to squeeze back and forth thereby periodically changing the effective interfacial area, hence generating alternating current. The alternating current is given to the rectifier design. There is no realistic model that has been developed yet for this technique. Thereby, a MATLAB based REWOD model is developed for the realistic simulation of the REWOD phenomenon. In the work, a comparison of different REWOD models such as planar surface, rough surface and porous models are performed demonstrating the variations in capacitance, current and voltage.
Mesh Networking for Inter-UAV Communications
Unmanned aerial systems (UASs) have a great potential to enhanced situational awareness in public safety operations. Many UASs operating in the same airspace can cause mid-air collisions. NASA and the FAA are developing a UAS traffic management (UTM) system, which could be used in public safety operations to manage the UAS airspace. UTM relies on an existing communication backhaul, however natural disasters may disrupt existing communications infrastructure or occur in areas where no backhaul exists. This thesis outlines a robust communications alternative that interfaces a fleet of UASs with a UTM service supplier (USS) over a mesh network. Additionally, this thesis outlines an algorithm for vehicle-to-vehicle discovery and communication over the mesh network.
Networking of UAVs Using 802.11s
The thesis simulates the problem of network connectivity that occurs due to the dynamic nature of a network during flight. Nine nodes are provided with initial positions and are flown based on the path provided by leader-follower control algorithm using the server-client model. The application layer provides a point to point connection between the server and client and by using socket programming in the transport layer, a server and clients are established. Each node performs a neighbor discovery to discover its neighbors in the data link layer and physical layer performs the CSMA/CA using RTS/CTS. Finally, multi hop routing is achieved in network layer. Each client connects with server at dedicated interval to share each other location and then moves to next location. This process is continued over a period of several iterations until the relative distance is achieved. The constraints and limitations of the technology are network connectivity is lack of flexibility for random location of nodes, links established with a distant node having single neighbor is unstable. Performance of a system decreases with increase in number of nodes.
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.
Real-Time Finger Spelling American Sign Language Recognition Using Deep Convolutional Neural Networks
This thesis presents design and development of a gesture recognition system to recognize finger spelling American Sign Language hand gestures. We developed this solution using the latest deep learning technique called convolutional neural networks. This system uses blink detection to initiate the recognition process, Convex Hull-based hand segmentation with adaptive skin color filtering to segment hand region, and a convolutional neural network to perform gesture recognition. An ensemble of four convolutional neural networks are trained with a dataset of 25254 images for gesture recognition and a feedback unit called head pose estimation is implemented to validate the correctness of predicted gestures. This entire system was developed using Python programming language and other supporting libraries like OpenCV, Tensor flow and Dlib to perform various image processing and machine learning tasks. This entire application can be deployed as a web application using Flask to make it operating system independent.
Implementation of Compressive Sampling for Wireless Sensor Network Applications
One of the challenges of utilizing higher frequencies in the RF spectrum, for any number of applications, is the hardware constraints of analog-to-digital converters (ADCs). Since mid-20th century, we have accepted the Nyquist-Shannon Sampling Theorem in that we need to sample a signal at twice the max frequency component in order to reconstruct it. Compressive Sampling (CS) offers a possible solution of sampling sub-Nyquist and reconstructing using convex programming techniques. There has been significant advancements in CS research and development (more notably since 2004), but still nothing to the advantage of everyday use. Not for lack of theoretical use and mathematical proof, but because of no implementation work. There has been little work on hardware in finding the realistic constraints of a working CS system used for digital signal process (DSP). Any parameters used in a system is usually assumed based on stochastic models, but not optimized towards a specific application. This thesis aims to address a minimal viable platform to implement compressive sensing if applied to a wireless sensor network (WSN), as well as address certain parameters of CS theory to be modified depending on the application.
The Role of Eigenvalues of Parity Check Matrix in Low-Density Parity Check Codes
The new developments in coding theory research have revolutionized the application of coding to practical systems. Low-Density Parity Check (LDPC) codes form a class of Shannon limit approaching codes opted for digital communication systems that require high reliability. This thesis investigates the underlying relationship between the spectral properties of the parity check matrix and LDPC decoding convergence. The bit error rate of an LDPC code is plotted for the parity check matrix that has different Second Smallest Eigenvalue Modulus (SSEM) of its corresponding Laplacian matrix. It is found that for a given (n,k) LDPC code, large SSEM has better error floor performance than low SSEM. The value of SSEM decreases as the sparseness in a parity-check matrix is increased. It was also found from the simulation that long LDPC codes have better error floor performance than short codes. This thesis outlines an approach to analyze LDPC decoding based on the eigenvalue analysis of the corresponding parity check matrix.
Improving the Gameplay Experience and Guiding Bottom Players in an Interactive Mapping Game
In game based learning, motivating the players to learn by providing them a desirable gameplay experience is extremely important. However, it's not an easy task considering the quality of today's commercial non-educational games. Throughout the gameplay, the player should neither get overwhelmed nor under-challenged. The best way to do so is to monitor the player's actions in the game because these actions can tell the reason behind the player's performance. They can also tell about the player's lacking competencies or knowledge. Based on this information, in-game educational interventions in the form of hints can be provided to the player. The success of such games depends on their interactivity, motivational outlook and thus player retention. UNTANGLED is an online mapping game based on crowd-sourcing, developed by Reconfigurable Computing Lab, UNT for the mapping problem of CGRAs. It is also an educational game for teaching the concepts of reconfigurable computing. This thesis performs qualitative comparative analysis on gameplays of low performing players of UNTANGLED. And the implications of this analysis are used to provide recommendations for improving the gameplay experience for these players by guiding them. The recommendations include strategies to reach a high score and a compact solution, hints in the form of preset patterns and a clustering based approach.
Implementing Digital Logic Design Concepts Using Paper Electronics
This thesis presents the implementation of some of the basic concepts of digital logic design in a fun and creative way with the help of paper electronics. This involves circuit building on paper using conductive tape or conductive ink and circuit components as electronics craft materials. Paper electronics toolkit called circuit sticker microcontroller which is deployed by a company named Chibitronics and AT89C51 microcontroller were used for the computational functioning of the circuits built on paper. This can be used to teach the fundamentals of digital logic design to the students in their early stage of studies in an attractive way and can help them them gain a better understanding. This thesis can also be helpful in grabbing the attention of high school students and motivate them towards choosing the engineering discipline for their higher studies.
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.
Group Testing: A Practical Approach
Broadly defined, group testing is the study of finding defective items in a large set. In the medical infection setting, that implies classifying each member of a population as infected or uninfected, while minimizing the total number of tests.
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.
Urban surface characterization using LiDAR and aerial imagery.
Many calamities in history like hurricanes, tornado and flooding are proof to the large scale impact they cause to the life and economy. Computer simulation and GIS helps in modeling a real world scenario, which assists in evacuation planning, damage assessment, assistance and reconstruction. For achieving computer simulation and modeling there is a need for accurate classification of ground objects. One of the most significant aspects of this research is that it achieves improved classification for regions within which light detection and ranging (LiDAR) has low spatial resolution. This thesis describes a method for accurate classification of bare ground, water body, roads, vegetation, and structures using LiDAR data and aerial Infrared imagery. The most basic step for any terrain modeling application is filtering which is classification of ground and non-ground points. We present an integrated systematic method that makes classification of terrain and non-terrain points effective. Our filtering method uses the geometric feature of the triangle meshes created from LiDAR samples and calculate the confidence for every point. Geometric homogenous blocks and confidence are derived from TIN model and gridded LiDAR samples. The results from two representations are used in a classifier to determine if the block belongs ground or otherwise. Another important step is detection of water body, which is based on the LiDAR sample density of the region. Objects like tress and bare ground are characterized by the geometric features present in the LiDAR and the color features in the infrared imagery. These features are fed into a SVM classifier which detects bare-ground in the given region. Similarly trees are extracted using another trained SVM classifier. Once we obtain bare-grounds and trees, roads are extracted by removing the bare grounds. Structures are identified by the properties of non-ground segments. Experiments were conducted using LiDAR samples and Infrared imagery …
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.
Exploration Of Energy And Area Efficient Techniques For Coarse-grained Reconfigurable Fabrics
Coarse-grained fabrics are comprised of multi-bit configurable logic blocks and configurable interconnect. This work is focused on area and energy optimization techniques for coarse-grained reconfigurable fabric architectures. In this work, a variety of design techniques have been explored to improve the utilization of computational resources and increase energy savings. This includes splitting, folding, multi-level vertical interconnect. In addition to this, I have also studied fully connected homogeneous and heterogeneous architectures, and 3D architecture. I have also examined some of the hybrid strategies of computation unit’s arrangements. In order to perform energy and area analysis, I selected a set of signal and image processing benchmarks from MediaBench suite. I implemented various fabric architectures on 90nm ASIC process from Synopsys. Results show area improvement with energy savings as compared to baseline architecture.
Hardware Implementation Of Conditional Motion Estimation In Video Coding
This thesis presents the rate distortion analysis of conditional motion estimation, a process in which motion computation is restricted to only active pixels in the video. We model active pixels as independent and identically distributed Gaussian process and inactive pixels as Gaussian-Markov process and derive the rate distortion function based on conditional motion estimation. Rate-Distortion curves for the conditional motion estimation scheme are also presented. In addition this thesis also presents the hardware implementation of a block based motion estimation algorithm. Block matching algorithms are difficult to implement on FPGA chip due to its complexity. We implement 2D-Logarithmic search algorithm to estimate the motion vectors for the image. The matching criterion used in the algorithm is Sum of Absolute Differences (SAD). VHDL code for the motion estimation algorithm is verified using ISim and is implemented using Xilinx ISE Design tool. Synthesis results for the algorithm are also presented.
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.
Modeling and Design of Antennas for Loosely Coupled Links in Wireless Power Transfer Applications
Wireless power transfer (WPT) systems are important in many areas, such as medical, communication, transportation, and consumer electronics. The underlying WPT system is comprised of a transmitter (TX) and receiver (RX). For biomedical applications, such systems can be implemented on rigid or flexible substrates and can be implanted or wearable. The efficiency of a WPT system is based on power transfer efficiency (PTE). Many WPT system optimization techniques have been explored to achieve the highest PTE possible. These are based on either a figure-of-merit (FOM) approach, quality factor (Q-factor) maximization, or by sweeping values for coil geometries. Four WPT systems for biomedical applications are implemented with inductive coupling. The thesis later presents an optimization technique for finding the maximum PTE of a range of frequencies and coil shapes through frequency, geometry and shape sweeping. Five optimized TX coil designs for different operating frequencies are fabricated for three shapes: square, hexagonal, and octagonal planar-spirals. The corresponding RX is implemented on polyimide tape with ink-jet-print (IJP) silver. At 80 MHz, the maximum measured PTE achieved is 2.781% at a 10 mm distance in the air for square planar-spiral coils.
Realization of LSTM Based Cognitive Radio Network
This thesis presents the realization of an intelligent cognitive radio network that uses long short term memory (LSTM) neural network for sensing and predicting the spectrum activity at each instant of time. The simulation is done using Python and GNU Radio. The implementation is done using GNU Radio and Universal Software Radio Peripherals (USRP). Simulation results show that the confidence factor of opportunistic users not causing interference to licensed users of the spectrum is 98.75%. The implementation results demonstrate high reliability of the LSTM based cognitive radio network.
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.
An Interactive Tool to Investigate the Inference Performance of Network Dynamics From Data
Network structure plays a significant role in determining the performance of network inference tasks. An interactive tool to study the dependence of network topology on estimation performance was developed. The tool allows end-users to easily create and modify network structures and observe the performance of pole estimation measured by Cramer-Rao bounds. The tool also automatically suggests the best measurement locations to maximize estimation performance, and thus finds its broad applications on the optimal design of data collection experiments. Finally, a series of theoretical results that explicitly connect subsets of network structures with inference performance are obtained.
The Effect of Mobility on Wireless Sensor Networks
Wireless sensor networks (WSNs) have gained attention in recent years with the proliferation of the micro-electro-mechanical systems, which has led to the development of smart sensors. Smart sensors has brought WSNs under the spotlight and has created numerous different areas of research such as; energy consumption, convergence, network structures, deployment methods, time delay, and communication protocols. Convergence rates associated with information propagations of the networks will be questioned in this thesis. Mobility is an expensive process in terms of the associated energy costs. In a sensor network, mobility has significant overhead in terms of closing old connections and creating new connections as mobile sensor nodes move from one location to another. Despite these drawbacks, mobility helps a sensor network reach an agreement more quickly. Adding few mobile nodes to an otherwise static network will significantly improve the network’s ability to reach consensus. This paper shows the effect of the mobility on convergence rate of the wireless sensor networks, through Eigenvalue analysis, modeling and simulation.
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.
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.
Distributed Source Coding with LDPC Codes: Algorithms and Applications
The syndrome source coding for lossless data compression with side information based on fixed-length linear block codes is the main emphasis of this work. We demonstrate that the source entropy rate can be achieved for syndrome source coding with side information when the sources are correlated. Next, we examine employing LDPC codes to apply the channel and syndrome concepts in order to satisfy the Slepian Wolf limit. Our findings indicate that irregular codes perform significantly better when the compression ratio is larger. Additionally, we looked at how well different applications performed when running on two different mobile networks. We have tested those applications which are used in our day-to-day life. Our main focus is to make wireless communication much easier. We know that nowadays data is increasing which led to increase in the transfer of data. There are a lot of errors while doing so like channel error, bit error rate, jitter, etc. To overcome such kind of problems compression and decompression should be done effectively without any complexity to achieve a high performance ratio.
Analysis of Compressive Sensing and Hardware Implementation of Orthogonal Matching Pursuit
My thesis is to understand the concept of compressive sensing algorithms. Compressive sensing will be a future alternate technique for the Nyquist rate, specific to some applications where sparsity property plays a major role. Software implementation of compressive sensing (CS) takes more time to reconstruct a signal from CS measurements, so we use the orthogonal matching pursuit and basis pursuit algorithms. We have used an image size of 256x256 is used for reconstruction and also implemented a field-programmable gate array (FPGA) of the orthogonal matching pursuit using an image.
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.
Estimation of Drone Location Using Received Signal Strength Indicator
The main objective of this thesis is to propose a UAV (also called as drones) location estimation system based on LoRaWAN using received signal strength indicator in a GPS denied environment. The drones are finding new applications in areas such as surveillance, search, rescue missions, package delivery, and precision agriculture. Nearly all applications require the localization of UAV during flight. Localization is the method of determining a UAVs physical position using a real or virtual coordinate system. This thesis proposes a LoRaWAN-based UAV location method and presents experimental findings from a prototype. The thesis mainly consists of two different sections: one is the distance estimation and the other is the location estimation. First, the distance is estimated based on the mean RSSI values which are recorded at the ground stations using the path loss model. Later using the slant distance estimation technique, the path loss model parameters L and C are estimated whose values are unknown at the beginning. These values completely depend on the environment. Finally, the trilateration system architecture is employed to find the 3-D location of the UAV.
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.
Group Testing with Greedy Algorithm
Group testing is all about identifying properties of a set of elements by testing them.
An Analysis of Compressive Sensing and the Electrocardiogram
As technology has advanced, data has become more and more important. The more breakthroughs are achieved, the more data is needed to support them. As a result, more storage is required in the system's memory. Compression is therefore required. Before it can be stored, the data must be compressed. To ensure that information is not lost, efficient compression is necessary. This also makes sure that there is no redundancy in the data that is being kept and stored. Compressive sensing has emerged as a new field of compression thanks to developments in sparse optimization. Rather than relying just on compression and sensing formulations, the theory blends the two. The objective of this thesis is to analyze the concept of compressive sensing and to study several reconstruction algorithms. Additionally, a few of the algorithms were put into practice. This thesis also included a model of the ECG, which is vital in determining the health of the heart. For the most part, the ECG is utilized to diagnose heart illness, and a modified synthetic ECG can be used to mimic some of these arrhythmias.
Emotion Recognition Using EEG Signals
Emotions have significant importance in human life in learning, decision-making, daily interaction, and perception of the surrounding environment. Hence, it has become very essential to detect and recognize a person's emotional states and to build a connection between humans and computers. This process is called brain-computer interaction (BCI) and is a vast field of research in neuroscience. Hence, in the past few years, emotion recognition has gained adequate attention in the research community. In this thesis, an emotion recognition system is designed and analyzed using EEG signals. Several existing feature extraction techniques are studied, analyzed, and implemented to extract features from the EEG signals. An SVM classifier is used to classify the features into various emotional states. Four emotional states are detected, namely, happy, sad, anger, and relaxed state. The model is tested, and simulation results are presented with an interpretation. Furthermore, this study has mentioned and discussed the efficacy of the results achieved. The findings from this study could be beneficial in developing emotion-sensitive technologies, such as augmented modes of communication for severely disabled individuals who are unable to communicate their feelings directly.
Prisoner's Dilemma in Quantum Perspective
It is known that quantum strategies change the range of possible payoffs for the players in the prisoner's dilemma. In this paper, we examine the effect of the degree of entanglement in determining the payoffs. When both players play quantum strategies, we show that the payoff for both players is unaffected by the entanglement value and it leads to a new Nash equilibrium.
Wireless Power Transfer (WPT) System Design for Freely-Moving Animals for Optogenetic Neuromulation Applications
Wireless power transfer (WPT) is currently the most efficient way for transmission of power from one port to another, that is popularly used in various applications.This technique can change the previous energy utilization methods in various applications such as electronic devices, implanted medical devices, electrical vehicles and so forth.It mainly helps overcome the limitations of short battery life, limited storage, heavy weight, and high cost of batteries.This paper is based on the design of a transmitter and a receiver to achieve wireless power transfer for applications like optogenetic stimulation in rodents. With inductive coupling, a very high efficiency can be achieved between the transmitting and receiving coils of an antenna at small distances. When the transmitter and receiver are strongly coupled and are working at their resonant frequencies, the range of efficient WPT can be extended. In this work, the simulations are performed in HFSS at a resonating frequency of 13.56 MHz.A 4-port transmitter and a single-port planar receiver model are developed in HFSS, and the simulations are performed to graph the S parameters with a separation distance of 4cm. A Wilkinson power divider is designed using ADS to split the power from the four ports of the transmitter. The design is simulated to compare the S21 at different positions on the TX.
Air Corridors: Concept, Design, Simulation, and Rules of Engagement
Air corridors are an integral part of the advanced air mobility infrastructure. They are the virtual highways in the sky for transportation of people and cargo in the controlled airspace at an altitude of around 1000 ft. to 2000 ft. above the ground level. This paper presents fundamental insights into the design of air corridors with high operational efficiency as well as zero collisions. It begins with the definitions of air cube, skylane or track, intersection, vertiport, gate, and air corridor. Then, a multi-layered air corridor model is proposed. Traffic at intersections is analyzed in detail with examples of vehicles turning in different directions. The concept of capacity of an air corridor is introduced along with the nature of distribution of locations of vehicles in the air corridor and collision probability inside the corridor are discussed. Finally, the results of simulations of traffic flows are presented.
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