Browsing by Subject "Sensors"

Now showing 1 - 6 of 6
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    Demand Controlled Ventilation Energy Savings for Air Handling Units
    (2021-12) Blubaugh, Matthew; Chen, Jie; Razban, Ali; Goodman, David
    Heat, cooling, and ventilation units are major energy consumers for commercial buildings, they can consume as much as 50% of the total annual power usage of a building. Coherent management of an air handling system’s energy is a key factor of reducing the energy costs and CO2 emissions that are associated with the demand for ventilating and conditioning the air in a building. The issue is that buildings are frequently over ventilated as a full assessment of the air handling unit (AHU) data is not evaluated by building operators. According to ASHRAE standards there are three key parameters that control indoor air quality (IAQ); these are the temperature, humidity, and CO2. Commonly occupancy setpoints implemented by building operators are focused on temperature and humidity control while neglecting the CO2 levels and their impact. While this may seem insignificant additional data proves to be important and can assist with energy management. Additionally, it can develop awareness of implementable procedures which conserve energy. Furthermore, data is not monitored in regard to the continuous assessment of the energy consumption with respect to analysis of opportunities to implement energy saving control strategies. By using these standards as a guide an AHUs energy can be managed more effectively by measuring the data and assessing the outputs compared to the standard. Previous research has shown that up to 75% savings for the ventilation fan energy is achievable when taking into account ASHRAE ventilation standards and controlling outside air ventilation, however, this research has omitted investigating the savings for other energy consumers associated with AHU’s operation. In order to assess the demand, it is required that the CO2 levels of the occupied zones be measured, and the outdoor air ventilation rate be adjusted based on real-time demand. The goal of the research is to assess the number of CO¬2 sensors needed to accurately measure demand-based needs for ventilation and determine an algorithm that will help building operators assess the energy savings by implementing demand-controlled ventilation (DCV) procedures. The scope of this research is to identify what sensors at minimum are required to collect the most pertinent data for implementation of a comprehensive energy saving algorithms and assess the impact on energy consumption of AHUs when demand-controlled ventilation procedures are implemented.
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    Design and Implementation of Sensing Methods on One-Tenth Scale of an Autonomous Race Car
    (2020-12) Veeramachaneni, Harshitha; Li, Lingxi; Chen, Yaobin; El-Sharkawy, Mohamed; King, Brian
    Self-driving is simply the capacity of a vehicle to drive itself without human intervention. To accomplish this, the vehicle utilizes mechanical and electronic parts, sensors, actuators and an AI computer. The on-board PC runs advanced programming, which permits the vehicle to see and comprehend its current circumstance dependent on sensor input, limit itself in that climate and plan the ideal course from point A to point B. Independent driving is not an easy task, and to create self-sufficient driving arrangements is an exceptionally significant ability in the present programming designing field. ROS is a robust and versatile communication middle ware (framework) tailored and widely used for robotics applications. This thesis work intends to show how ROS could be used to create independent driving programming by investigating self-governing driving issues, looking at existing arrangements and building up a model vehicle utilizing ROS. The main focus of this thesis is to develop and implement a one-tenth scale of an autonomous RACECAR equipped with Jetson Nano board as the on-board computer, PCA9685 as PWM driver, sensors, and a ROS based software architecture. Finally, by following the methods presented in this thesis, it is conceivable to build an autonomous RACECAR that runs on ROS. By following the means portrayed in this theory of work, it is conceivable to build up a self-governing vehicle.
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    Development of Chemiresistor Based Nanosensors to Detect Volatile Cancer Biomarkers
    (2019-05) Vij, Shitiz; Agarwal, Mangilal; Anwar, Sohel; Towar, Andres
    Researchers have shown links between various hydrocarbons and carbonyl compounds and diseases, such as cancer using exhaled breath analysis through gas chromatography/mass spectroscopy (GC/MS) analysis of volatile organic compounds (VOCs). Trained canines can detect these VOCs and can differentiate a patient suffering from cancer from a healthy control patient. In this project, an attempt has been made to develop highly sensitive sensors for the detection of low concentrations of aldehyde VOCs, such as nonanal, using conductive polymer composites (CPCs) and functionalized gold nanoparticles (f-GNPs). Facile methods have been used to enhance the sensitivity and cross-selectivity of the fabricated sensors towards nonanal. Interdigitated electrodes (IDEs) are fabricated through a photolithography process. Sensors of PEI/carbon black (CB) composite were developed via spin-coating of the material followed by the heat treatment process. Sensors of 1-Mercapto-(triethylene glycol) methyl ether functionalized GNPs are developed via drop-casting of nanomaterial and f-GNP/PEI sensors are fabricated by spin casting PEI film on top of f-GNPs. Fourier Transform Infrared (FTIR) analysis, X-Ray Diffraction (XRD) analysis, contact angle measurement, and Field Emission Scanning Electron Microscopy (FESEM) analysis was conducted to characterize the fabricated devices. The fabricated sensors have been tested with a low concentration of nonanal, nonanone, dodecane, and 1-octanol in dry air. Multiple sensors are fabricated to ensure sensors reproducibility. The sensors have been exposed repeatedly to the targeting VOC toxiv assess the repeatability of the sensors. PEI/CB sensor degradation was studied over a period of 36 days. The fabricated PEI/CB film could detect (1-80 ppm) of nonanal with higher selectivity, than the f-GNPs. The sensor0s sensitivity to nonanal was over fourteen times higher than 2-nonanone, 1-octanol, and dodecane. This shows the high selectivity of the fabricated sensor toward nonanal. In addition, the proposed sensor maintained its sensitivity to nonanal over time showing minimal degradation. The sensor response to nonanal at a relative humidity (RH) of 50% and 85% dropped less than 13% and 32% respectively. The Response of f-GNP sensors to nonanal (400 ppb - 15 ppm), dodecane (5 - 15 ppm), 1-octanol (5 - 15 ppm), and 2-nonanone (5 - 15 ppm) presented a sensitivity (∆R=R0) of 0.217%, 0.08%, 0.192% and 0.182% per ppm of the VOCs respectively. Despite the high sensitivity to the targeting VOCs, the fabricated sensors were damaged in an environment with relative humidity (RH) at 45%. A thin layer of PEI over the film was developed to ensure the sensor could tolerate longtime exposure to water vapor in an environment with RH up to 85% and enhance the sensor selectivity towards nonanal. The f-GNP/PEI sensors with nonanal (400 ppb- 15 ppm), dodecane (100 -200 ppm), 1-octanol (5 - 15 ppm) and 2-nonanone (5 - 15 ppm) presented sensitivity (∆R=R0) of 0.21%, 0.017%, 0.0438% and 0.0035% per ppm of the VOCs respectively.
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    Exceptional points in anti-PT symmetric system of delay coupled semiconductor lasers
    (SPIE, 2021-08) Vemuri, Gautam; Wilkey, Andrew; Joglekar, Yogesh; Physics, School of Science
    This paper will report on some features of a platform for the realization of an anti parity-time (anti-PT) symmetric system in a pair of time-delay coupled semiconductor lasers, with special emphasis on the delay induced dynamics in the system. The system is modeled by a modified Lang-Kobayashi rate equations model, augmented to include delayed coupling. The role of a phase accumulation factor that arises from the delayed coupling is elucidated. Finally, the novel exceptional point(s) behavior that is characteristic of the time-delay is investigated via numerics as well as analytically via the Lambert W function.
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    Integration of UAVS with Real Time Operating Systems and Establishing a Secure Data Transmission
    (2019-08) Ravi, Niranjan; El-Sharkawy, Mohamed; King, Brian; Rizkalla, Maher
    In today’s world, the applications of Unmanned Aerial Vehicle (UAV) systems are leaping by extending their scope from military applications on to commercial and medical sectors as well. Owing to this commercialization, the need to append external hardware with UAV systems becomes inevitable. This external hardware could aid in enabling wireless data transfer between the UAV system and remote Wireless Sensor Networks (WSN) using low powered architecture like Thread, BLE (Bluetooth Low Energy). The data is being transmitted from the flight controller to the ground control station using a MAVlink (Micro Air Vehicle Link) protocol. But this radio transmission method is not secure, which may lead to data leakage problems. The ideal aim of this research is to address the issues of integrating different hardware with the flight controller of the UAV system using a light-weight protocol called UAVCAN (Unmanned Aerial Vehicle Controller Area Network). This would result in reduced wiring and would harness the problem of integrating multiple systems to UAV. At the same time, data security is addressed by deploying an encryption chip into the UAV system to encrypt the data transfer using ECC (Elliptic curve cryptography) and transmitting it to cloud platforms instead of radio transmission.
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    Plant Level IIoT Based Energy Management Framework
    (2023-05) Koshy, Liya Elizabeth; Chien, Stanley Yung-Ping; Chen, Jie; King, Brian
    The Energy Monitoring Framework, designed and developed by IAC, IUPUI, aims to provide a cloud-based solution that combines business analytics with sensors for real-time energy management at the plant level using wireless sensor network technology. The project provides a platform where users can analyze the functioning of a plant using sensor data. The data would also help users to explore the energy usage trends and identify any energy leaks due to malfunctions or other environmental factors in their plant. Additionally, the users could check the machinery status in their plant and have the capability to control the equipment remotely. The main objectives of the project include the following: • Set up a wireless network using sensors and smart implants with a base station/ controller. • Deploy and connect the smart implants and sensors with the equipment in the plant that needs to be analyzed or controlled to improve their energy efficiency. • Set up a generalized interface to collect and process the sensor data values and store the data in a database. • Design and develop a generic database compatible with various companies irrespective of the type and size. • Design and develop a web application with a generalized structure. Hence the database can be deployed at multiple companies with minimum customization. The web app should provide the users with a platform to interact with the data to analyze the sensor data and initiate commands to control the equipment. The General Structure of the project constitutes the following components: • A wireless sensor network with a base station. • An Edge PC, that interfaces with the sensor network to collect the sensor data and sends it out to the cloud server. The system also interfaces with the sensor network to send out command signals to control the switches/ actuators. • A cloud that hosts a database and an API to collect and store information. • A web application hosted in the cloud to provide an interactive platform for users to analyze the data. The project was demonstrated in: • Lecture Hall (https://iac-lecture-hall.engr.iupui.edu/LectureHallFlask/). • Test Bed (https://iac-testbed.engr.iupui.edu/testbedflask/). • A company in Indiana. The above examples used sensors such as current sensors, temperature sensors, carbon dioxide sensors, and pressure sensors to set up the sensor network. The equipment was controlled using compactable switch nodes with the chosen sensor network protocol. The energy consumption details of each piece of equipment were measured over a few days. The data was validated, and the system worked as expected and helped the user to monitor, analyze and control the connected equipment remotely.