Intelligent sensors.

Food availability and food waste are signi cant global problems which can be
mitigated through the use of sensor networks. Current methods of monitoring food
waste require manual data collection and are implemented infrequently, providing
imprecise information. The use of sensors to automate food waste measurement
allows constant monitoring, provides a better dataset for analysis, and enables real-
time feedback, which can be used to affect behavioral change in consumers. The
data from such networks can be used to drive ambient displays designed to educate
a target audience, and ultimately reduce the amount of waste generated. We present
WASTE REDUCE, a system for automating the measurement of food waste and
affecting behavioral change. The challenges and results of deploying such a system
are presented. To assess the bene ts of using WASTE REDUCE, two case studies
are conducted. The rst study evaluates three different displays, and the second
reevaluates one of these displays in a separate location. These studies con rm that
the combination of automated monitoring and ambient feedback can reduce food
waste for targeted groups.

In this research, a wind feedforward (FF) controller has been developed to augment closed loop feedback controllers for the position and heading station keeping control of Unmanned Surface Vehicles (USVs). The performance of the controllers was experimentally tested using a 16 foot USV in an outdoor marine environment. The FF controller was combined with three nonlinear feedback controllers, a Proportional–Derivative (PD) controller, a Backstepping (BS) controller, and a Sliding mode (SM) controller, to improve the station-keeping performance of the USV. To address the problem of wind model uncertainties, adaptive wind feedforward (AFF) control schemes are also applied to the FF controller, and implemented together with the BS and SM feedback controllers. The adaptive law is derived using Lyapunov Theory to ensure stability. On-water station keeping tests of each combination of FF and feedback controllers were conducted in the U.S. Intracoastal Waterway in Dania Beach, FL USA. Five runs of each test condition were performed; each run lasted at least 10 minutes. The experiments were conducted in Sea State 1 with an average wind speed of between 1 to 4 meters per second and significant wave heights of less than 0.2 meters. When the performance of the controllers is compared using the Integral of the Absolute Error (IAE) of position criterion, the experimental results indicate that the BS and SM feedback controllers significantly outperform the PD feedback controller (e.g. a 33% and a 44% decreases in the IAE, respectively). It is also found that FF is beneficial for all three feedback controllers and that AFF can further improve the station keeping performance. For example, a BS feedback control combined with AFF control reduces the IAE by 25% when compared with a BS feedback controller combined with a non-adaptive FF controller. Among the eight combinations of controllers tested, SM feedback control combined with AFF control gives the best station keeping performance with an average position and heading error of 0.32 meters and 4.76 degrees, respectively.