The team designed the Roboscope Cart as a transportation and outdoor alignment system for large-diameter telescopes under individual operation.

The autonomous cart can carry a 1000-pound telescope up to 200 meters while remaining stable and upright, thus protecting telescope, mount, and other accessories. Once at a viewing site, the cart deploys, levels and autoaligns north in less than 10 minutes.

The cart allows astronomers to transport larger telescopes with ease and eliminates the need for a permanent dome.

The team’s goal is to design and build a patient-tracking system to optimize the progress of patients through their hospitalization. The system designed increases hospital productivity by allowing healthcare professionals to easily see patient status without extra consultations, saving patients time and hospitals money.

The team developed an iOS-based mobile application that communicates with Bluetooth beacons that read tags attached to patients as wearable devices. The application uses Wi-Fi to send and receive data to a server via the hospital’s local area network.

The application allows healthcare professionals to determine if patients are ready to be discharged and approximately how long they have been waiting. The application also computes and recommends to the user the fastest route to a patient, according to a customizable algorithm that takes into account distance, wait time, floor layout and other relevant variables.

The team designed an autonomous launcher that deploys and navigates the sponsor’s rescue boat, EMILY, to someone it has detected in the water. The launcher uses a thermal sensor to detect the presence of a person in its detection range and collect data about their location.

Upon detecting a person in distress, the launcher’s deployment system turns on its visual and audio alert system and launches EMILY into the water. After deployment, the thermal camera continuously updates the person’s position while a dual GPS system guides EMILY to the person’s location. After the person boards EMILY, an operator is required to complete the rescue.

The operator can pull in EMILY and the rescued person manually by attached rope, or activate the launcher’s winch to pull them at a controlled pace.

This project’s objective is to reduce the time taken for someone experiencing sudden cardiac arrest to receive medical attention, because their chance of survival decreases by 10 percent every minute after the onset of the attack. The team achieved this goal by designing and building a neighborhood network of automatic external defibrillators, or AEDs.

When someone on the AED network experiences a cardiac attack, they press a button on the in-home network device. This signals their neighbors’ devices, which produce an audio and flashing light alert similar to a fire alarm.

The address of the person experiencing the attack is displayed on the device screen of the responder, who takes the AED to the caller’s home and administers it. The device of the person experiencing the attack also calls 911 so someone can talk to a dispatcher about the medical emergency.

This project has the objective of improving illumination methods for underwater photography beyond a simple array of bright white LED lights, which can produce backscattering and are not optimized optically for the subsea environment.

Based on the sponsor’s requirements, and building upon previous work done by the sponsor, the team designed a trade study to analyze and differentiate between advanced illumination methods such as off-axis illumination, linear and circular polarization of right- and left-handedness, multiple LED wavelengths, and beam shaping.

The team designed software and an experiment to quantitatively measure image contrast in order to determine which illumination method or combination of methods best improved the quality of the image.

The project aims to design a system to remotely control the sponsor’s lab experiments over the Internet. The system consists of several independent and modular linear movement stages that can be assembled in any configuration or anywhere on a standard optical table.

Stages can be built with a variety of movement distances, from 100 to 1000 millimeters of linear travel, ensuring versatility for a wide range of experiments. Stages connect to the electronics box to receive power and communication, or daisy chain to another stage. The electronics box is also the interface between the computer web server system and the stage experiment assembly.

Because all the stages’ mechanical interfaces are the same, any optics components that can be installed on a standard optical table can be installed on the stages and be moved by remote control over an internet connection.

Detection and characterization of moving objects in Earth orbit to identify their origin, intent and nature is vital for protecting critical space assets. Designing a dedicated sensor system tailored to these special requirements would allow collection of data critical to the success of the UA Space Object Behavior Sciences initiative.

The goal of this project is to refurbish the optical components of a preexisting telescope to create a new optical tube assembly that will be placed on an equatorial fork mount. The primary technical challenge was to create a Serrurier truss that can accommodate the existing optics.

The new optical tube assembly has a universal interface that matches with any type of mount used for this system. The team manufactured two optical tube assemblies that will be mated with the mount. The system will interface with customer-furnished correctional optics.

The project objective is design of a computational model for severe traumatic brain injury to assist with prognosis in a clinical setting such as in an intensive care unit. Current methods of developing prognoses are subjective, not necessarily reliable, and fail to capture the dynamics of injury progression.

The model incorporates features from multiple modalities, such as physiological processes and clinical data from laboratory tests. A method called “soft computing” is incorporated into the model’s algorithm, which is based on human logic and provides a prognosis from the patient’s clinical data.

Model prognoses include the state of the patient, based on symptoms and initial lab values, and which laboratory values and events are most likely to occur within a few days of admission.

Macadamia nuts are typically harvested at the end of the season, which decreases nut quality and sale price. The team designed an autonomous nut harvester that ensures a regular harvest cycle and requires minimal operator monitoring. A preprogrammed path is uploaded to the GPS-connected autonomous navigation system. The harvester follows this path after a single initialization by the operator.

A sensor prevents the harvester from colliding with obstacles, including humans and animals, by stopping harvester operation until the obstacle has moved. In an emergency the harvester alerts the operator via a smart device. A weight sensor alerts the navigation system when the bed is nearly full, and the harvester pauses on its route. It then heads to the dispensing location, dispenses its load through the base of the harvester, and returns to the route.

This comprehensive harvesting prototype reduces demand for traditional machinery, harvests faster, and minimizes human intervention and overhead costs.

The project’s goal is to improve the pollination rates of medjool date palms at a date farm in Yuma, Arizona, by designing and creating a semiautonomous unmanned pollination aircraft.

The current pollination method involves tying a nylon stocking filled with pollen to an unmanned aircraft that is flown over the palm, and letting the wind deliver the pollen to the trees. Farmers determine wind direction by kicking dirt into the air, and coordinate their approximations with the unmanned aircraft pilot to estimate the fall pattern of pollen. The team’s improved pollen-delivery system includes an automated pollinator that protects pollen spores from inclement weather and reduces pollen waste by dropping a precise payload.

Unmanned aircraft flight is semiautonomous, with a user interface that integrates camera input with weather station and ground-control data input to avoid collisions, determine flower maturity, and determine ideal aircraft location relative to the palm tree.