The versatility of the raspberry pi allows it to be used in a multitude of ways, specifically Raspberry Pi’s can be used to build modular weather stations to monitor the environment conditions in multiple areas. This project is the creation of a network of raspberry pi’s taking temperature, pressure, and humidity data in the Advanced Particle Detection Lab (APDL) run by the physics department at Texas Tech. An undetermined amount of sensors, each consisting of a raspberry pi and BME280 sensor, make up the data collection end of our system each sensor sends their data back to a hub server which then aggregates the data and allows it to be analyzed at a centrally located terminal. The climate control of the APDL is important because the lab is manufacturing environmentally sensitive radiation sensors, and such the HVAC systems must be monitored closely. In addition to the rooms, there are sensors placed in two dry cabinets that must be kept below a certain humidity level. The goal is for this project to complete the network created such that this technology could be duplicated and used by other research labs looking for environmental monitoring.
My involvement in this project currently is to create a secure network of all the sensors and raspberry pi's that allow for the transfer of data in the correct pipeline using the MQTT framework. I have set up the MQTT server with broker enabled publish-subscribe system to direct the data stream to central hub Pi. I also created the dashboard for our lab website for real time monitoring of the "weather" conditions. I have used apache as the web server and MySql as the backend of the website to serve as a database for the sensor data and used a php based secure system to facilitate management of the database. Moreover, I aided in the electrical assembly of the sensor enabled microcontroller by soldering various components and engineered the calibration and installation of 12 sensor enabled Raspberry Pi’s to record data at a user‑defined rate. More recently, I haved developed an AI enabled alarm system that notifies the stakeholders of the lab through texts and emails using the SMTP library
Poster and Oral Presentations:
Advisor:Nural Akchurin, PhD
Muons are elementary particles able to penetrate deep into structures, allowing us to image through very dense material. The muons we interact with are created in the Earth’s upper atmosphere from cosmic rays colliding with the nuclei of air molecules. These high-energy collisions result in pions, which often decay into muons and muon neutrinos. The muon flux reaching the Earth’s surface is about 10,000 muons per minute per square meter, making them the most abundant cosmic ray particle at sea level. These charged particles lose energy mostly through inelastic collisions in materials. Many of these collisions can take place per given path length, causing substantial energy loss. The energy loss per unit length (-dE/dx) is generally given by the Bethe-Bloch formula3 and is used to describe the density-dependent energy loss in materials. Muon tomography is a technique that uses this fact and the measured muon flux to reconstruct images. Muon tomography has been used to image large objects such as volcanoes, buildings, and ancient archaeological structures for more than 50 years. In his pioneering work, Alvarez searched for hidden structures in an Egyptian pyramid in 1970, thus showing the noninvasive feature of muon tomography and its applications.
The goal of this project is to develop a portable muon telescope that is capable of the best possible angular resolution that is physically attainable. Developing such a system requires an efficient muon detector, communication electronics, and excellent software for reconstructing the muon trajectory. Our first prototype telescope (Phase I) was built at the Advance Particle Detector Lab at Reese Technology Center in the summer of 2019 for training purposes.
For this project, I assisted with the development of a Neural Network Architecture (Asymmetric Deep Mixture Density NN)that predicts muon hit locations from photon time propagation with a 87% accuracy. Presently, I am working on developing and implementing high fidelity readout schemes using FPGA systems for applications in Phase II Muon Telescopes.
- SA Shanto, K Binu, S Cano, M Howard, C Gabriel, C Moreno, V Bradley, Machine Learning Applications in Muon Tomography, in prep