No abstract

No abstract

In this paper we discuss determining canine pose in the context of common poses observed in Urban Search and Rescue dogs through the use a sensor network made up of accelerometers. We discuss the use of the Canine Pose Estimation System in a disaster environment, and propose techniques for determining canine pose. In addition we discuss the challenges with this approach in such environments. This paper presents the experimental results obtained from the Heavy Urban Search and Rescue disaster simulation, where experiments were conducted using multiple canines, which show that angles can be derived from acceleration readings. Our experiments show that similar angles were measured for each of the poses, even when measured on multiple USAR canines of varying size. We also developed an algorithm to determine poses and display the current canine pose to the screen of a laptop. The algorithm was successful in determining some poses and had difficulty with others. These results are presented and discussed in this paper.

In an environment where node density is massive, placement is heterogeneous and redundant sensory traffic is produced; limited network resources such as bandwidth and energy are hastily consumed by individual sensor nodes. Equipped with only a limited battery power supply, this minimizes the lifetime of these sensor nodes. At the network layer, many researchers have tackled this issue by proposing several energy efficient routing schemes. All these schemes tend to save energy by elevating redundant data traffic via in-network processing and choosing empirically good and shortest routing paths for transfer of sensory data to a central location (sink) for further, application-specific processing. Seldom has an attempt been made to reduce network traffic by moving the application-specific code to the source nodes. We unmitigated our efforts to augment the node lifetime within a sensor network by introducing mobile agents. These mobile agents can be used to greatly reduce communication costs, especially over low bandwidth links, by moving the processing function to the data rather than bringing the data to a central processor. Toward this end, we propose an agent-based directed diffusion approach to increase sensor node efficiency and we present the experimental results.

No abstract

This paper describes Canine Augmentation Technology (CAT) for use in urban search and rescue (USAR). CAT is a WiFi enabled sensor array that is worn by a trained canines deployed in urban disasters. The system includes, but is not limited to, cameras that provide emergency responders with real-time data to remotely monitor, analyze and take action during USAR operations. An analysis is made of the current tools available to USAR workers including rescue robots and canine search teams. From this analysis came the design of CAT-a system that extracts the strengths of each available USAR tool and combines them to compliment each other. Our experiments yield promising results that CAT may provide significant help to rescuers.

In this paper we discuss the use of accelerometers and Bluetooth to monitor canine pose in the context of common poses observed in Urban Search and Rescue dogs. We discuss the use of the Canine Pose System in a disaster environment, and propose techniques for determining canine pose. In addition we discuss the challenges with this approach in such environments. The paper presents the experimental results obtained from the Heavy Urban Search and Rescue disaster simulation, where experiments were conducted using multiple canines, which show that angles can be derived from acceleration readings. Our experiments show that similar angles were measured for each of the poses, even when measured on multiple USAR canines of varying size. We also found measurable and consistent differences between each of the poses, making them clearly distinguishable from one another, again even when comparing with different USAR canines.

No abstract