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.

Cattle ranchers in desert environments have to set up expansive water networks for their herds to survive. The integrity of these networks is critical and they need to be checked often.

The goal of this project is to design a system to monitor water tanks on an open range and communicate water levels to the rancher remotely. The system designed by the team measures water level and relays the information to the rancher through a line-of-sight transmission system.

Grid power is not available on the open range, so a power source is installed with the data collection and transmission systems at the tanks. A remote water-measurement system reduces the need for ranchers to visit and inspect their tanks in person.

The demand for improvements in vehicle navigation and automation can exceed the capabilities of GPS, which is not always available. The goal of this project is to evaluate the use of radar for vehicle navigation in GPS-deprived environments.

The navigation system designed by the team integrates two ViaSat radar modules with a student-designed mounting system, interfacing circuit board, and system to collect data for postprocessing. In postprocessing, the system calculates instantaneous velocities from the collected data and uses those velocities to infer the navigation of the vehicle.

The radar-based vehicle navigation system could operate when GPS is unavailable, and if used in conjunction with GPS could lead to more precise and accurate vehicle navigation systems.