Optical current sensors (OCSs) are initially developed to measure relatively large current over a wide range of frequency band. They are also used as protective devices in the event a fault occurs due to a short circuit, in the power generation and distribution industries. The basic principal used in OCS is the Faraday effect. When a light guiding faraday medium is placed in a magnetic field which is produced by the current flowing in the conductor around the magnetic core, the plane of polarization of the linearly polarized light is rotated. The angle of rotation is proportional to the magnetic field strength, proportionality constant and the interaction length. The proportionality constant is the Verdet constant V (λ, T), which is dependent on both temperature and wavelength of the light. Opto electrical methods are used to measure the angle of rotation of the polarization plane. By measuring the angle the current flowing in the current carrying conductor can be calculated. But the accuracy of the OCS is lost of the angle of rotation of the polarization plane is dependent on the Verdet constant, apart from the magnetic field strength. As temperature increases the Verdet constant decreases, so the angle of rotation …

The survival rate for a suddent cardiac arrest (SCA) is incredibly low, with less than one in ten surviving; most SCAs occur outside of a hospital setting. There is a need to develop an effective and efficient system that can sense, communicate and remediate potential SCA situations on a near real-time basis. This research presents a novel Zeolite-PDMS-based optically unobtrusive flexible dry electrodes for biosignal acquisition from various subjects while at rest and in motion. Two zeolite crystals (4A and 13X) are used to fabricate the electrodes. Three different sizes and two different filler concentrations are compared to identify the better performing electrode suited for electrocardiogram (ECG) data acquisition. A low-power, low-noise amplifier with chopper modulation is designed and implemented using the standard 180nm CMOS process. A commercial off-the-shelf (COTS) based wireless system is designed for transmitting ECG signals. Further, this dissertation provides a framework for Machine Learning Classification algorithms on large, open-source Arrhythmia and SCA datasets. Supervised models with features as the input data and deep learning models with raw ECG as input are compared using different methods. The machine learning tool classifies the datasets within a few minutes, saving time and effort for the physicians. The experimental results …

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.

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 …

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.

In large-scale civil structures, a limited number of sensors are placed to monitor the health of civil structures to reduce maintenance, communication and energy costs. In this thesis, the problem of optimal sensor location placement to infer the health of civil structures is explored. First, a comparative study of approaches from the fields of control engineering and civil engineering is conducted . The widely used civil engineering approaches such as effective independence (EI) and modal assurance criterion (MAC) have limitations because of the negligence of modes and damping parameters. On the other hand, control engineering approaches consider the entire system dynamics using impulse response-type sensor measurement data. Such inference can be formulated as an estimation problem, with the dynamics formulated as a second-order differential equation. The comparative study suggests that damping dynamics play significant impact to the selection of best sensor location---the civil engineering approaches that neglect the damping dynamics lead to very different sensor locations from those of the control engineering approaches. In the second part of the thesis, an initial attempt to directly connect the topological graph of the structure (that defines the damping and stiffness matrices) and the second-order dynamics is conducted.

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.

An economically reliable technique for the synthesis of silicon nanowire was developed using silicon chloride as source material. The 30-40 micron long nanowires were found to have diameters ranging from 40 – 100 nm. An amorphous oxide shell covered the nanowires, post-growth. Raman spectroscopy confirmed the composition of the shell to be silicon-dioxide. Photoluminescence measurements of the as-grown nanowires showed green emission, attributed to the presence of the oxide shell. Etching of the oxide shell was found to decrease the intensity of green emission. n-type doping of the silicon nanowires was achieved using antimony as the dopant. The maximum dopant concentration was achieved by post-growth diffusion. Intrinsic nanowire parameters were determined by implementation of the as-grown and antimony doped silicon nanowires in field effect transistor configuration.

A wireless sensor network (WSN) is a spatially distributed network used to monitor the physical and environmental conditions such as temperature, pressure, sound, humidity, heat, etc. WSNs can be modeled using different simulation frameworks like OMNeT++, Prowler, Atarraya, PiccSIM, Network Simulator, etc. In this research, Simulink framework was used to model WSN system. The complete WSN consisting of transmitting nodes, communication channel, and receiver nodes are built in the Simulink framework. Orthogonal frequency division multiplexing technique was used to transmit the information. The implemented wireless sensor system behavior is studied using temperature as the measurement parameter at different values of signal to noise ratio. The plots of bit error rate versus signal to noise ratio and frame error rate versus signal to noise ratio are generated in the Simulink framework. It is easy to study the effect of different physical layer parameters on the performance of wireless sensor networks by implementing WSN in the Simulink framework.

ZigBee is a substandard of IEEE 802.15 family that is specially designed to take care of factors such as power, data rate and area that primarily affect network performance. This has controlling and monitoring capability, which finds potential applications in different sectors. ZigBee allows the concept of hybrid networks and mobility. A comprehensive analysis of ZigBee networks was carried out by constructing and simulating the networks to evaluate the performance in terms of throughput, delay, network load, and packets dropped. This research is aimed at evaluating the effect of network topology on the system performance. A careful review of simulation platforms brought the conclusion of using OPNET Modeler which has the required frame work. Different network topologies of simple and hybrid were built and simulated. Throughout the simulations, the best-case scenarios were drawn to the conclusion by the graphical analysis of parameters of evaluation. Mobile networks were constructed and simulated to investigate the effect of mobility on communication.

The ways that people use libraries have changed drastically over the past few decades. Proliferation of computers and the internet have led to the purpose of libraries expanding from being only places where information is stored, to spaces where people teach, learn, create, and collaborate. Due to this, the ways that people occupy the space in a library have also changed. To keep up with these changes and improve patron experience, institutions collect data to determine how their spaces are being used. This thesis involves the development a system that collects, stores, and analyzes data relevant to occupancy to learn how a space is being utilized. Data is collected from a temperature and humidity sensor, passive Infrared sensor, and an Infrared thermal sensor array to observe people as they occupy and move through a space. Algorithms were developed to analyze the collected sensor data to determine how many people are occupying a space or the directions that people are moving through a space. The algorithms demonstrate the ability to track multiple people moving through a space as well as count the number of people in a space with an RMSE of roughly 0.39 people.

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.

This dissertation presents beamforming designs. Using novel techniques and methods, the performance of the beamforming is improved on dual-band, tri-band, flexible function, tunable function in THz, and dynamic controllability on incident wave.

This thesis presents an optimized drive signal control system for a 2.5 GHz Doherty power amplifier (PA). The designed system enables independent control of the amplitudes and phases of the drive signals fed to the inputs of two parallel PAs. This control system is demonstrated here for Doherty PA architecture with a combiner network which is used as an impedance inversion between the path of two parallel connected PAs. Independent control of the inputs is achieved by incorporating a variable attenuator (VA) and a variable phase shifter (VPS) in each of the two parallel paths. Integrating VA and VPS allows driving varying power levels with an arbitrary phase difference between the individual parallel PAs. A Combiner network consists of a quarter-wave transmission line at the output of the main power amplifier, which is used to invert the impedance between the main and peaking transistor. The specific VA (Qorvo QPC6614) and VPS (Qorvo QPC2108) components that are used for the test system provide an amplitude attenuation range from 0.5 dB to 31.5 dB with a step size of 0.5 dB and a phase range from 0◦ to 360◦ for a step size of 5.6◦at the intended operating frequency of 2.5 GHz, …

In general, Dual-Band technology enables microwave components to work at two different frequencies. This thesis introduces novel dual-band microwave components and their applications. Chapter 2 presents a novel compact dual-band balun (converting unbalanced signals to balanced ones). The ratio between two working frequencies is analyzed. A novel compact microstrip crossover (letting two lines to cross each other with very high isolation) and its dual-band application is the subject of chapter 3. A dual-frequency cloak based on lumped LC-circuits is introduced in chapter 4. In chapter 5, a dual-band RF device to detect dielectric constant changes of liquids in polydimethylsiloxane (PDMS) microfluidic channels has been presented. Such a device is very sensitive, and it has significantly improved the stability. Finally, conclusion of this thesis and future works are given in chapter 6.

Layered structures are commonly used in communication systems, but their roles in decentralized control are not understood well. In the first part of this thesis, a theoretical study of consensus (a typical decentralized control task) in layered structures is conducted. The unique graph topology approach permits explicit characterization of consensus performance based on simple graphical characteristics of MLMG structures. In the second part of this thesis, a generic LEGO test-bed to mimic multi-domain communication with layered structures is described. A search-and-rescue scenario is implemented to demonstrate the use of the test-bed.

RSA cryptography is one of the most widely used cryptosystems in the world. FPGA/ASIC implementations for the classic RSA cryptosystem have high resource utilization due to the use of the Extended Euclid's algorithm for MOD inverse generation, the MOD exponent operation for encryption and decryption, and through non finite-field arithmetic. This thesis translates the RSA cryptosystem into the finite-field domain of arithmetic which greatly increases the range of encryption and decryption keys and replaces the MOD exponent with a multiplication. A new algorithm, the SPX algorithm, is presented and shown to outperform Euclid's algorithm, which is the most widely used mechanism to compute the GCD in FPGA implementations of RSA. The SPX algorithm is then extended to support the computation of the MOD inverse and supply decryption keys. Lastly, a finite-field RSA system is created and shown to support character encryption and decryption while being designed to be integrated into any larger system.

Over-the-air programming systems for wireless sensor networks have drawbacks that stem from fundamental limitations in the hardware used in current sensor nodes. Also, advances in technology make it feasible to use capacitors as the sole energy storage mechanism for sensor nodes using energy harvesting, but most current designs require additional electronics. These two considerations led to the design of a new sensor node. A microcontroller was chosen that meets the Popek and Goldberg virtualization requirements. The hardware design for this new sensor node is presented, as well as a preliminary operating system. The prototypes are tested, and demonstrated to be sustainable with a capacitor and solar panel. The issue of capacitor leakage is considered and measured.

The focus of this thesis is on developing theories and techniques to extract lossy microwave filter parameters from data. In the literature, the Cauchy methods have been used to extract filters’ characteristic polynomials from measured scattering parameters. These methods are described and some examples are constructed to test their performance. The results suggest that the Cauchy method does not work well when the Q factors representing the loss of filters are not even. Based on some prototype filters and the relationship between Q factors and the loss, we conduct preliminary studies on alternative representations of the characteristic polynomials. The parameters in these new models are extracted using the Levenberg–Marquardt algorithm to accurately estimate characteristic polynomials and the loss information.

This thesis investigates the design and implementation of turbo codes over the GNU radio. The turbo codes is a class of iterative channel codes which demonstrates strong capability for error correction. A software defined radio (SDR) is a communication system which can implement different modulation schemes and tune to any frequency band by means of software that can control the programmable hardware. SDR utilizes the general purpose computer to perform certain signal processing techniques. We implement a turbo coding system using the Universal Software Radio Peripheral (USRP), a widely used SDR platform from Ettus. Detail configuration and performance comparison are also provided in this research.

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.

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.

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.

The purpose of this research is not to research new technology but how to improve existing technology and understand how the manufacturing process works. Reliability Engineering fall under the category of Quality Control and uses predictions through statistical measurements and life testing to figure out if a specific manufacturing technique will meet customer satisfaction. The research also answers choice of materials and choice of manufacturing process to provide a device that will not only meet but exceed customer demand. Reliability Engineering is one of the final testing phases of any new product development or redesign.