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) 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 …

The key requirements of a reliable neural signal recording system include low power to support long-term monitoring, low noise, minimum tissue damage, and wireless transmission. The neural spikes are also detected and sorted on-chip/off-chip to implement closed-loop neuromodulation in a high channel count setup. All these features together constitute an empirical neural recording system for neuroscience research. In this prospectus, we propose to develop a neural signal acquisition system with wireless transmission and feature extraction. We start by designing a prototype entirely built with commercial-off-the-shelf components, which includes recording and wireless transmission of synthetic neural data and feature extraction. We then conduct the CMOS implementation of the low-power multi-channel neural signal recording read-out circuit, which enables the in-vivo recording with a small form factor. Another direction of this thesis is to design a self-powered motion tracking read-out circuit for wearable sensors. As the wearable industry continues to advance, the need for self-powered medical devices is growing significantly. In this line of research, we propose a self-powered motion sensor based on reverse electrowetting-on-dielectric (REWOD) with low-power integrated electronics for remotely monitoring health conditions. We design the low-power read-out circuit for a wide range of input charges, which is generated from the …

In recent years, deep learning based computer vision technology has developed rapidly. This is not only due to the improvement of computing power, but also due to the emergence of high-quality datasets. The combination of object detectors and drones has great potential in the field of rescue and disaster relief. We created an image dataset specifically for vision applications on drone platforms. The dataset contains 5000 images, and each image is carefully labeled according to the PASCAL VOC standard. This specific dataset will be very important for developing deep learning algorithms for drone applications. In object detection models, loss function plays a vital role. Considering the uneven distribution of large and small objects in the dataset, we propose adjustment coefficients based on the frequencies of objects of different sizes to adjust the loss function, and finally improve the accuracy of the model.

Electrocardiogram (ECG) is a biological signal that represents the heart's electrical activity. Interference from power lines introduces a frequency component of 50 to 60 Hz into the signal, which is the principal cause of ECG corruption. By using the Cadence Virtuoso Spectre circuit simulator and typical TSMC RF 180 nm CMOS technology, a notch filter was created to reduce powerline interference. The advantage of utilizing a notch filter for PLI is that noise at 60 Hz is completely eliminated without sacrificing any important information. Additionally, this study contains a MATLAB-based model for, which is used to compute the power spectral density for the obtained time-domain signal. By incorporating power spectral density into data gathering procedures, it is feasible to enhance data collection methodologies, construct models that appropriately account for observed power and aid in the removal of undesired components. NI LabVIEW is used to extract features. The advantage of ECG feature extraction is that it provides information that assists in the identification of cardiac rhythm issues, and gives information about the occurrence of heart attack. In this study, several patient data sets are utilized to extract characteristics and provide information regarding heart condition abnormalities.

The main objective of this thesis is to propose a localization method for a UAV using various computer vision and machine learning techniques. It plays a major role in planning the strategy for the flight, and acts as a navigational contingency method, in event of a GPS failure. The implementation of the algorithms employs high processing capabilities of the graphics processing unit, making it more efficient. The method involves the working of various neural networks, working in synergy to perform the localization. This thesis is a part of a collaborative project between The University of North Texas, Denton, USA, and the University of Windsor, Ontario, Canada. The localization has been divided into three phases namely object detection, recognition, and location estimation. Object detection and position estimation were discussed in this thesis while giving a brief understanding of the recognition. Further, future strategies to aid the UAV to complete the mission, in case of an eventuality, like the introduction of an EDGE server and wireless charging methods, was also given a brief introduction.

Currently, there is no equipment to measure the real-time fit of EHMR or N-95masks which are used in harsh environments. Improper fit of these EHMRs or N-95 masks exposes the personnel to hazardous environments. Surface acoustic wave (SAW) sensors have been around for few decades and are being used in various applications. In this work, real-time PM2.5 detection using passive wireless SAW sensors is presented. The design of meander antenna at 433MHz for wireless interrogation of SAW sensor using HFSS and ADS is also presented in this thesis. This works also includes the design of YZ-lithium niobate SAW sensor including COMSOL simulation.

This thesis consists of two projects in the field of machine learning. Previous research in the OSCAR UNT lab based on KMeans color quantization is further developed and applied to individual color channels and segmented input images to explore compression rates while still maintaining high output image quality. The second project implements a small-scale dual path network for image classifiaction utilizing the CIFAR-10 dataset containing 60,000 32x32 pixel images ranging across ten categories.

This dissertation is particularly focused on a novel approach of a wirelessly powered neuromodulation system for chronic patients. The inductively coupled transmitter (TX) and receiver (RX) coils are designed through optimization to achieve maximum efficiency. A power management unit (PMU) consisting of a voltage rectifier, voltage regulator along with a stimulation circuitry is also designed to provide pulse stimulation to genetically modified neurons. For continuous health monitoring purposes, the response from the brain due to stimulation needs to be recorded and transmitted wirelessly outside the brain for analysis. A low-power high-data duty-cycled impulse-radio ultra-wideband (IR-UWB) transmitter is designed and implemented using the standard CMOS process. Another focus of this dissertation is the design of a reverse electrowetting-on-dielectric (REWOD) based energy harvesting circuit for wearable sensor applications which is capable of generating a very low-frequency signal from motion activity such a walking, running, jogging, etc. A commercial off-the-shelf (COTS) based and on-chip based energy harvesting circuit is designed for very low-frequency signals. The experimental results show promising progress towards the advancement in the wirelessly powered neuromodulation system and building the self-powered wearable sensor.

The feedback control system is defined as the sampling of an output signal and feeding it back to the input, resulting in an error signal that drives the overall system. This dissertation focuses on the stabilization and performance of state feedback control systems. Chapters 3 and 4 focus on the feedback control protocol approaching in the multi-agents system. In particular, the global regulation of distributed optimization problems has been considered. Firstly, we propose a distributed optimization algorithm based on the proportional-integral control strategy and the exponential convergence rate has been delivered. Moreover, a decentralized mechanism has been equipped to the proposed optimization algorithm, which enables an arbitrarily chosen agent in the system can compute the value of the optimal solution by only using the successive local states. After this, we consider the cost function follows the restricted secant inequality. A dynamic event-triggered mechanism design has been proposed. By ensuring the global regulation of the distributed proportional-integral optimization algorithm, the dynamic event-triggered mechanism efficiently reduces the communication frequency among agents. Chapter 5 focuses on the feedback control protocol approaching the single-agent system. Specifically, we investigate the truncated predictor feedback control of the regulation of linear input-delayed systems. For the purpose of …

Networked multi-agent systems have attracted lots of researchers to develop algorithms, techniques, and applications.A multi-agent networked system consists of more than one subsystem (agent) to cooperately solve a global problem with only local computations and communications in a fully distributed manner. These networked systems have been investigated in various different areas including signal processing, control system, and machine learning. We can see massive applications using networked systems in reality, for example, persistent surveillance, healthcare, factory manufacturing, data mining, machine learning, power system, transportation system, and many other areas. Considering the nature of those mentioned applications, traditional centralized control and optimization algorithms which require both higher communication and computational capacities are not suitable. Additionally, compared to distributed control and optimization approaches, centralized control, and optimization algorithms cannot be scaled into systems with a large number of agents, or guarantee performance and security. All of the limitations of centralized control and optimization algorithms motivate us to investigate and develop new distributed control and optimization algorithms in networked systems. Moreover, convergence rate and analysis are crucial in control and optimization literature, which motivates us to investigate how to analyze and accerlate the convergence of distributed optimization algorithms.

Data acquisition through wireless sensor networks (WSNs) has enormous potential for scalable, distributed, real-time observations of monitored environmental parameters. Despite increasing versatility and functionalities, one critical factor that affects the operation of WSNs is limited power. WSN sensor nodes are usually battery powered, and therefore the long-term operation of the WSN greatly depends on battery capacity and the node's power consumption rate. This thesis focuses on WSN node design to reduce power consumption in order to achieve sustainable power supply. For this purpose, this thesis proposes a Moteino-based WSN node and an energy efficient duty cycle that reduces current consumption in standby mode using an enhanced watchdog timer. The nodes perform radio communication at 915 MHz, for short intervals (180ms) every 10 minutes, and consume 6.8 mA at -14dBm. For testing, the WSN node monitored a low-power combined air temperature, relative humidity, and barometric pressure sensor, together with a typical soil moisture sensor that consumes more power. Laboratory tests indicated average current consumption of ~30µA using these short radio transmission intervals. After transmission tests, field deployment of a star-configured network of nine of these nodes and one gateway node provides a long-term platform for testing under rigorous conditions. A webserver …

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, …

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 …

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.

Memory is an important part of any digital processing system. On-chip SRAM can be found in various levels of the memory hierarchy in a processor and occupies a considerable area of the chip. Leakage is one of the challenges which shrinking of technology has introduced and the leakage of SRAM constitutes a substantial part of the total leakage power of the chip due to its large area and the fact that many of the cells are idle without any access. In this thesis, we introduce asymmetric SRAM cells using stacked transistors which reduce the leakage up to 26% while increasing the delay of the cell by only 1.2% while reducing the read noise margin of the cell by only 15.7%. We also investigate an asymmetric cell configuration in which increases the delay by 33% while reduces the leakage up to 30% and reducing the read noise margin by only 1.2% compared to a regular SRAM cell.