Matching Items (13)
Filtering by
- All Subjects: wireless
- All Subjects: Internet of Things
- Creators: Electrical Engineering Program
- Creators: Zhang, Yanchao
Description
Every communication system has a receiver and a transmitter. Irrespective if it is wired or wireless.The future of wireless communication consists of a massive number of transmitters and receivers. The question arises, can we use computer vision to help wireless communication? To satisfy the high data requirement, a large number of antennas are required. The devices that employ large-antenna arrays have other sensors such as RGB camera, depth camera, or LiDAR sensors.These vision sensors help us overcome the non-trivial wireless communication challenges, such as beam blockage prediction and hand-over prediction.This is further motivated by the recent advances in deep learning and computer vision that can extract high-level semantics from complex visual scenes, and the increasing interest of leveraging machine/deep learning tools in wireless communication problems.[1] <br/><br/>The research was focused solely based on technology like 3D cameras,object detection and object tracking using Computer vision and compression techniques. The main objective of using computer vision was to make Milli-meter Wave communication more robust, and to collect more data for the machine learning algorithms. Pre-build lossless and lossy compression algorithms, such as FFMPEG, were used in the research. An algorithm was developed that could use 3D cameras and machine learning models such as YOLOV3, to track moving objects using servo motors and low powered computers like the raspberry pi or the Jetson Nano. In other words, the receiver could track the highly mobile transmitter in 1 dimension using a 3D camera. Not only that, during the research, the transmitter was loaded on a DJI M600 pro drone, and then machine learning and object tracking was used to track the highly mobile drone. In order to build this machine learning model and object tracker, collecting data like depth, RGB images and position coordinates were the first yet the most important step. GPS coordinates from the DJI M600 were also pulled and were successfully plotted on google earth. This proved to be very useful during data collection using a drone and for the future applications of position estimation for a drone using machine learning. <br/><br/>Initially, images were taken from transmitter camera every second,and those frames were then converted to a text file containing hex-decimal values. Each text file was then transmitted from the transmitter to receiver, and on the receiver side, a python code converted the hex-decimal to JPG. This would give an efect of real time video transmission. However, towards the end of the research, an industry standard, real time video was streamed using pre-built FFMPEG modules, GNU radio and Universal Software Radio Peripheral (USRP). The transmitter camera was a PI-camera. More details will be discussed as we further dive deep into this research report.
ContributorsSeth, Madhav (Author) / Alkhateeb, Ahmed (Thesis director) / Alrabeiah, Muhammad (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
The team has designed and built a golf swing analyzer that informs the user of his mistakes while putting with a golf club. The team also interfaced a Linux program with the analyzer that allows the user to review the flaws in his golf swing. In addition, the application is more personalized than existing devices and tailored to the individual based on his level of experience. The analyzer consists of an accelerometer, gyroscope, magnetometer, vibration motor, and microcontroller that are connected on a board that attaches to the top of the shaft of a golf club, fitting inside a 3D printed case. The team has assembled all of the necessary hardware, and is able to successfully display critical parameters of a golf putt, as well as send instant feedback to the user. The final budget for this project was $378.24
ContributorsKaur, Hansneet (Co-author) / Cox, Jeremy (Co-author) / Farnsworth, Chad (Co-author) / Zorob, Nabil (Co-author) / Chae, Junseok (Thesis director) / Aberle, James (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
Description
Every engineer is responsible for completing a capstone project as a culmination of accredited university learning to demonstrate technical knowledge and enhance interpersonal skills, like teamwork, communication, time management, and problem solving. This project, with three or four engineers working together in a group, emphasizes not only the importance of technical skills acquired through laboratory procedures and coursework, but the significance of soft skills as one transitions from a university to a professional workplace; it also enhances the understanding of an engineer's obligation to ethically improve society by harnessing technical knowledge to bring about change. The CC2541 Smart SensorTag is a device manufactured by Texas Instruments that focuses on the use of wireless sensors to create low energy applications, or apps; it is equipped with Bluetooth Smart, which enables it to communicate wirelessly with similar devices like smart phones and computers, assisting greatly in app development. The device contains six built-in sensors, which can be utilized to track and log personal data in real-time; these sensors include a gyroscope, accelerometer, humidifier, thermometer, barometer, and magnetometer. By combining the data obtained through the sensors with the ability to communicate wirelessly, the SensorTag can be used to develop apps in multiple fields, including fitness, recreation, health, safety, and more. Team SensorTag chose to focus on health and safety issues to complete its capstone project, creating applications intended for use by senior citizens who live alone or in assisted care homes. Using the SensorTag's ability to track multiple local variables, the team worked to collect data that verified the accuracy and quality of the sensors through repeated experimental trials. Once the sensors were tested, the team developed applications accessible via smart phones or computers to trigger an alarm and send an alert via vibration, e-mail, or Tweet if the SensorTag detects a fall. The fall detection service utilizes the accelerometer and gyroscope sensors with the hope that such a system will prevent severe injuries among the elderly, allow them to function more independently, and improve their quality of life, which is the obligation of engineers to better through their work.
ContributorsMartin, Katherine Julia (Author) / Thornton, Trevor (Thesis director) / Goryll, Michael (Committee member) / Electrical Engineering Program (Contributor) / School of Film, Dance and Theatre (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
Description
The Lightning Audio capstone group, consisting of Brian Boerhinger, Rahul Nandan, Jaime Ramirez, and Niccolo Magnotto (myself), united in the effort to prove the feasibility of a consumer grade plasma arc speaker. This was achieved in group's prototype design, which demonstrates the potential for a refined product in its conventional interfacing, casing, size, safety, and aesthetics. If the potential for an excellent ionization-based loudspeaker product were realized, it would be highly profitable in its reasonable cost of production, novelty, and place in a large and fitting market.
ContributorsMagnotto, Niccolo John (Author) / Roedel, Ronald (Thesis director) / Huffman, James (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2014-05
Description
In the world we live in today, nothing is impossible. Due to the advancements of technology, humans around the globe are able to hold computers that fit within the size of their pocket. These computers can do marvelous things, however run off batteries. These batteries need to be charged and up until a little while ago there was only one option available: wired chargers; however, because of the advancement of technology society has created a way to transfer power via magnetic fields. Now this concept has been around for a long time since the days of Nikola Tesla but just recently society has been able to apply his discoveries to charging these computers in our pockets. Unfortunately, the current models of these chargers come with a drawback as they are less efficient than wired chargers. However, this is the question our group has set out to answer. Is there any way possible to improve the efficiency of these wireless chargers so they are equal or even more efficient than wired chargers. This paper explores how to improve the efficiency in wireless chargers. Through research, simulations and testing the group has discovered areas that efficiency can be improved as well as makes recommendations to change the current wireless chargers on the market today. This paper also explores future applications of wireless chargers that can not only make life much easier but could also save lives in some cases. These applications can have many effects on hospitality, the medical field, as well as the supply chain and logistics of America.
ContributorsMcCulley, Matthew Alan (Co-author) / Cole, Kennedy (Co-author) / Chickamenahalli, Shamala (Thesis director) / Chakrabarti, Chaitali (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
The purpose of this project is to analyze the MIT OpenCourseWare coffee can radar design and modify it to be better suited for drone based synthetic aperture radar (SAR) applications while maintaining the low-cost aspect of the original design. The MIT coffee can radar can function as a ranged radar, a Doppler radar, or as SAR. Through simulations and research, the suggestions for how to modify the radar resulted in swapping the coffee can monopole antennas for patch antenna arrays or helical ordinary end-fire antennas, adding an Arduino for automatic recording of output pulses, and switching from a breadboard construction to a PCB to shrink form factor and keep costs and construction time low.
ContributorsRivera, Danielle (Author) / Trichopoulos, Georgios (Thesis director) / Aberle, James (Committee member) / Department of Information Systems (Contributor) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-12
Description
The use of conventional weather radar in vulcanology leads to two problems: the radars often use wavelengths which are too long to detect the fine ash particles, and they cannot be field–adjusted to fit the wide variety of eruptions. Thus, to better study these geologic processes, a new radar must be developed that is easily reconfigurable to allow for flexibility and can operate at sufficiently short wavelengths.
This thesis investigates how to design a radar using a field–programmable gate array board to generate the radar signal, and process the returned signal to determine the distance and concentration of objects (in this case, ash). The purpose of using such a board lies in its reconfigurability—a design can (relatively easily) be adjusted, recompiled, and reuploaded to the hardware with none of the cost or time overhead required of a standard weather radar.
The design operates on the principle of frequency–modulated continuous–waves, in which the output signal frequency changes as a function of time. The difference in transmit and echo frequencies determines the distance of an object, while the magnitude of a particular difference frequency corresponds to concentration. Thus, by viewing a spectrum of frequency differences, one is able to see both the concentration and distances of ash from the radar.
The transmit signal data was created in MATLAB®, while the radar was designed with MATLAB® Simulink® using hardware IP blocks and implemented on the ROACH2 signal processing hardware, which utilizes a Xilinx® Virtex®–6 chip. The output is read from a computer linked to the hardware through Ethernet, using a Python™ script. Testing revealed minor flaws due to the usage of lower–grade components in the prototype. However, the functionality of the proposed radar design was proven, making this approach to radar a promising path for modern vulcanology.
This thesis investigates how to design a radar using a field–programmable gate array board to generate the radar signal, and process the returned signal to determine the distance and concentration of objects (in this case, ash). The purpose of using such a board lies in its reconfigurability—a design can (relatively easily) be adjusted, recompiled, and reuploaded to the hardware with none of the cost or time overhead required of a standard weather radar.
The design operates on the principle of frequency–modulated continuous–waves, in which the output signal frequency changes as a function of time. The difference in transmit and echo frequencies determines the distance of an object, while the magnitude of a particular difference frequency corresponds to concentration. Thus, by viewing a spectrum of frequency differences, one is able to see both the concentration and distances of ash from the radar.
The transmit signal data was created in MATLAB®, while the radar was designed with MATLAB® Simulink® using hardware IP blocks and implemented on the ROACH2 signal processing hardware, which utilizes a Xilinx® Virtex®–6 chip. The output is read from a computer linked to the hardware through Ethernet, using a Python™ script. Testing revealed minor flaws due to the usage of lower–grade components in the prototype. However, the functionality of the proposed radar design was proven, making this approach to radar a promising path for modern vulcanology.
ContributorsLee, Byeong Mok (Co-author) / Xi, Andrew Jinchi (Co-author) / Groppi, Christopher (Thesis director) / Mauskopf, Philip (Committee member) / Baumann, Alicia (Committee member) / Cochran, Douglas (Committee member) / Electrical Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
The use of conventional weather radar in vulcanology leads to two problems: the radars often use wavelengths which are too long to detect the fine ash particles, and they cannot be field–adjusted to fit the wide variety of eruptions. Thus, to better study these geologic processes, a new radar must be developed that is easily reconfigurable to allow for flexibility and can operate at sufficiently short wavelengths.
This thesis investigates how to design a radar using a field–programmable gate array board to generate the radar signal, and process the returned signal to determine the distance and concentration of objects (in this case, ash). The purpose of using such a board lies in its reconfigurability—a design can (relatively easily) be adjusted, recompiled, and reuploaded to the hardware with none of the cost or time overhead required of a standard weather radar.
The design operates on the principle of frequency–modulated continuous–waves, in which the output signal frequency changes as a function of time. The difference in transmit and echo frequencies determines the distance of an object, while the magnitude of a particular difference frequency corresponds to concentration. Thus, by viewing a spectrum of frequency differences, one is able to see both the concentration and distances of ash from the radar.
The transmit signal data was created in MATLAB®, while the radar was designed with MATLAB® Simulink® using hardware IP blocks and implemented on the ROACH2 signal processing hardware, which utilizes a Xilinx® Virtex®–6 chip. The output is read from a computer linked to the hardware through Ethernet, using a Python™ script. Testing revealed minor flaws due to the usage of lower–grade components in the prototype. However, the functionality of the proposed radar design was proven, making this approach to radar a promising path for modern vulcanology.
This thesis investigates how to design a radar using a field–programmable gate array board to generate the radar signal, and process the returned signal to determine the distance and concentration of objects (in this case, ash). The purpose of using such a board lies in its reconfigurability—a design can (relatively easily) be adjusted, recompiled, and reuploaded to the hardware with none of the cost or time overhead required of a standard weather radar.
The design operates on the principle of frequency–modulated continuous–waves, in which the output signal frequency changes as a function of time. The difference in transmit and echo frequencies determines the distance of an object, while the magnitude of a particular difference frequency corresponds to concentration. Thus, by viewing a spectrum of frequency differences, one is able to see both the concentration and distances of ash from the radar.
The transmit signal data was created in MATLAB®, while the radar was designed with MATLAB® Simulink® using hardware IP blocks and implemented on the ROACH2 signal processing hardware, which utilizes a Xilinx® Virtex®–6 chip. The output is read from a computer linked to the hardware through Ethernet, using a Python™ script. Testing revealed minor flaws due to the usage of lower–grade components in the prototype. However, the functionality of the proposed radar design was proven, making this approach to radar a promising path for modern vulcanology.
ContributorsXi, Andrew Jinchi (Co-author) / Lee, Matthew Byeongmok (Co-author) / Groppi, Christopher (Thesis director) / Mauskopf, Philip (Committee member) / Cochran, Douglas (Committee member) / Baumann, Alicia (Committee member) / Electrical Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Internet of Things (IoT) is emerging as part of the infrastructures for advancing a large variety of applications involving connections of many intelligent devices, leading to smart communities. Due to the severe limitation of the computing resources of IoT devices, it is common to offload tasks of various applications requiring substantial computing resources to computing systems with sufficient computing resources, such as servers, cloud systems, and/or data centers for processing. However, this offloading method suffers from both high latency and network congestion in the IoT infrastructures.
Recently edge computing has emerged to reduce the negative impacts of tasks offloading to remote computing systems. As edge computing is in close proximity to IoT devices, it can reduce the latency of task offloading and reduce network congestion. Yet, edge computing has its drawbacks, such as the limited computing resources of some edge computing devices and the unbalanced loads among these devices. In order to effectively explore the potential of edge computing to support IoT applications, it is necessary to have efficient task management and load balancing in edge computing networks.
In this dissertation research, an approach is presented to periodically distributing tasks within the edge computing network while satisfying the quality-of-service (QoS) requirements of tasks. The QoS requirements include task completion deadline and security requirement. The approach aims to maximize the number of tasks that can be accommodated in the edge computing network, with consideration of tasks’ priorities. The goal is achieved through the joint optimization of the computing resource allocation and network bandwidth provisioning. Evaluation results show the improvement of the approach in increasing the number of tasks that can be accommodated in the edge computing network and the efficiency in resource utilization.
Recently edge computing has emerged to reduce the negative impacts of tasks offloading to remote computing systems. As edge computing is in close proximity to IoT devices, it can reduce the latency of task offloading and reduce network congestion. Yet, edge computing has its drawbacks, such as the limited computing resources of some edge computing devices and the unbalanced loads among these devices. In order to effectively explore the potential of edge computing to support IoT applications, it is necessary to have efficient task management and load balancing in edge computing networks.
In this dissertation research, an approach is presented to periodically distributing tasks within the edge computing network while satisfying the quality-of-service (QoS) requirements of tasks. The QoS requirements include task completion deadline and security requirement. The approach aims to maximize the number of tasks that can be accommodated in the edge computing network, with consideration of tasks’ priorities. The goal is achieved through the joint optimization of the computing resource allocation and network bandwidth provisioning. Evaluation results show the improvement of the approach in increasing the number of tasks that can be accommodated in the edge computing network and the efficiency in resource utilization.
ContributorsSong, Yaozhong (Author) / Yau, Sik-Sang (Thesis advisor) / Huang, Dijiang (Committee member) / Sarjoughian, Hessam S. (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2018
Description
Emerging from years of research and development, the Internet-of-Things (IoT) has finally paved its way into our daily lives. From smart home to Industry 4.0, IoT has been fundamentally transforming numerous domains with its unique superpower of interconnecting world-wide devices. However, the capability of IoT is largely constrained by the limited resources it can employ in various application scenarios, including computing power, network resource, dedicated hardware, etc. The situation is further exacerbated by the stringent quality-of-service (QoS) requirements of many IoT applications, such as delay, bandwidth, security, reliability, and more. This mismatch in resources and demands has greatly hindered the deployment and utilization of IoT services in many resource-intense and QoS-sensitive scenarios like autonomous driving and virtual reality.
I believe that the resource issue in IoT will persist in the near future due to technological, economic and environmental factors. In this dissertation, I seek to address this issue by means of smart resource allocation. I propose mathematical models to formally describe various resource constraints and application scenarios in IoT. Based on these, I design smart resource allocation algorithms and protocols to maximize the system performance in face of resource restrictions. Different aspects are tackled, including networking, security, and economics of the entire IoT ecosystem. For different problems, different algorithmic solutions are devised, including optimal algorithms, provable approximation algorithms, and distributed protocols. The solutions are validated with rigorous theoretical analysis and/or extensive simulation experiments.
I believe that the resource issue in IoT will persist in the near future due to technological, economic and environmental factors. In this dissertation, I seek to address this issue by means of smart resource allocation. I propose mathematical models to formally describe various resource constraints and application scenarios in IoT. Based on these, I design smart resource allocation algorithms and protocols to maximize the system performance in face of resource restrictions. Different aspects are tackled, including networking, security, and economics of the entire IoT ecosystem. For different problems, different algorithmic solutions are devised, including optimal algorithms, provable approximation algorithms, and distributed protocols. The solutions are validated with rigorous theoretical analysis and/or extensive simulation experiments.
ContributorsYu, Ruozhou, Ph.D (Author) / Xue, Guoliang (Thesis advisor) / Huang, Dijiang (Committee member) / Sen, Arunabha (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2019